Science.gov

Sample records for low-rank fuels symposium

  1. Proceedings of the sixteenth biennial low-rank fuels symposium

    SciTech Connect

    Not Available

    1991-01-01

    Low-rank coals represent a major energy resource for the world. The Low-Rank Fuels Symposium, building on the traditions established by the Lignite Symposium, focuses on the key opportunities for this resource. This conference offers a forum for leaders from industry, government, and academia to gather to share current information on the opportunities represented by low-rank coals. In the United States and throughout the world, the utility industry is the primary user of low-rank coals. As such, current experiences and future opportunities for new technologies in this industry were the primary focuses of the symposium.

  2. Transportation costs for new fuel forms produced from low rank US coals

    SciTech Connect

    Newcombe, R.J.; McKelvey, D.G. ); Ruether, J.A. )

    1990-09-01

    Transportation costs are examined for four types of new fuel forms (solid, syncrude, methanol, and slurry) produced from low rank coals found in the lower 48 states of the USA. Nine low rank coal deposits are considered as possible feedstocks for mine mouth processing plants. Transportation modes analyzed include ship/barge, pipelines, rail, and truck. The largest potential market for the new fuel forms is coal-fired utility boilers without emission controls. Lowest cost routes from each of the nine source regions to supply this market are determined. 12 figs.

  3. Task 27 -- Alaskan low-rank coal-water fuel demonstration project

    SciTech Connect

    1995-10-01

    Development of coal-water-fuel (CWF) technology has to-date been predicated on the use of high-rank bituminous coal only, and until now the high inherent moisture content of low-rank coal has precluded its use for CWF production. The unique feature of the Alaskan project is the integration of hot-water-drying (HWD) into CWF technology as a beneficiation process. Hot-water-drying is an EERC developed technology unavailable to the competition that allows the range of CWF feedstock to be extended to low-rank coals. The primary objective of the Alaskan Project, is to promote interest in the CWF marketplace by demonstrating the commercial viability of low-rank coal-water-fuel (LRCWF). While commercialization plans cannot be finalized until the implementation and results of the Alaskan LRCWF Project are known and evaluated, this report has been prepared to specifically address issues concerning business objectives for the project, and outline a market development plan for meeting those objectives.

  4. OXIDATION OF MERCURY ACROSS SCR CATALYSTS IN COAL-FIRED POWER PLANTS BURNING LOW RANK FUELS

    SciTech Connect

    Constance Senior

    2004-10-29

    This is the seventh Quarterly Technical Report for DOE Cooperative Agreement No: DE-FC26-03NT41728. The objective of this program is to measure the oxidation of mercury in flue gas across SCR catalyst in a coal-fired power plant burning low rank fuels using a slipstream reactor containing multiple commercial catalysts in parallel. The Electric Power Research Institute (EPRI) and Argillon GmbH are providing co-funding for this program. This program contains multiple tasks and good progress is being made on all fronts. During this quarter, a model of Hg oxidation across SCRs was formulated based on full-scale data. The model took into account the effects of temperature, space velocity, catalyst type and HCl concentration in the flue gas.

  5. OXIDATION OF MERCURY ACROSS SCR CATALYSTS IN COAL-FIRED POWER PLANTS BURNING LOW RANK FUELS

    SciTech Connect

    Constance Senior

    2004-04-30

    This is the fifth Quarterly Technical Report for DOE Cooperative Agreement No: DE-FC26-03NT41728. The objective of this program is to measure the oxidation of mercury in flue gas across SCR catalyst in a coal-fired power plant burning low rank fuels using a slipstream reactor containing multiple commercial catalysts in parallel. The Electric Power Research Institute (EPRI) and Argillon GmbH are providing co-funding for this program. This program contains multiple tasks and good progress is being made on all fronts. During this quarter, the available data from laboratory, pilot and full-scale SCR units was reviewed, leading to hypotheses about the mechanism for mercury oxidation by SCR catalysts.

  6. OXIDATION OF MERCURY ACROSS SCR CATALYSTS IN COAL-FIRED POWER PLANTS BURNING LOW RANK FUELS

    SciTech Connect

    Constance Senior; Temi Linjewile

    2003-10-31

    This is the third Quarterly Technical Report for DOE Cooperative Agreement No: DE-FC26-03NT41728. The objective of this program is to measure the oxidation of mercury in flue gas across SCR catalyst in a coal-fired power plant burning low rank fuels using a slipstream reactor containing multiple commercial catalysts in parallel. The Electric Power Research Institute (EPRI) and Argillon GmbH are providing co-funding for this program. This program contains multiple tasks and good progress is being made on all fronts. During this quarter, the second set of mercury measurements was made after the catalysts had been exposed to flue gas for about 2,000 hours. There was good agreement between the Ontario Hydro measurements and the SCEM measurements. Carbon trap measurements of total mercury agreed fairly well with the SCEM. There did appear to be some loss of mercury in the sampling system toward the end of the sampling campaign. NO{sub x} reductions across the catalysts ranged from 60% to 88%. Loss of total mercury across the commercial catalysts was not observed, as it had been in the March/April test series. It is not clear whether this was due to aging of the catalyst or to changes in the sampling system made between March/April and August. In the presence of ammonia, the blank monolith showed no oxidation. Two of the commercial catalysts showed mercury oxidation that was comparable to that in the March/April series. The other three commercial catalysts showed a decrease in mercury oxidation relative to the March/April series. Oxidation of mercury increased without ammonia present. Transient experiments showed that when ammonia was turned on, mercury appeared to desorb from the catalyst, suggesting displacement of adsorbed mercury by the ammonia.

  7. OXIDATION OF MERCURY ACROSS SCR CATALYSTS IN COAL-FIRED POWER PLANTS BURNING LOW RANK FUELS

    SciTech Connect

    Constance Senior; Temi Linjewile

    2003-07-25

    This is the first Quarterly Technical Report for DOE Cooperative Agreement No: DE-FC26-03NT41728. The objective of this program is to measure the oxidation of mercury in flue gas across SCR catalyst in a coal-fired power plant burning low rank fuels using a slipstream reactor containing multiple commercial catalysts in parallel. The Electric Power Research Institute (EPRI) and Ceramics GmbH are providing co-funding for this program. This program contains multiple tasks and good progress is being made on all fronts. During this quarter, analysis of the coal, ash and mercury speciation data from the first test series was completed. Good agreement was shown between different methods of measuring mercury in the flue gas: Ontario Hydro, semi-continuous emission monitor (SCEM) and coal composition. There was a loss of total mercury across the commercial catalysts, but not across the blank monolith. The blank monolith showed no oxidation. The data from the first test series show the same trend in mercury oxidation as a function of space velocity that has been seen elsewhere. At space velocities in the range of 6,000-7,000 hr{sup -1} the blank monolith did not show any mercury oxidation, with or without ammonia present. Two of the commercial catalysts clearly showed an effect of ammonia. Two other commercial catalysts showed an effect of ammonia, although the error bars for the no-ammonia case are large. A test plan was written for the second test series and is being reviewed.

  8. Process for clean-burning fuel from low-rank coal

    DOEpatents

    Merriam, Norman W.; Sethi, Vijay; Brecher, Lee E.

    1994-01-01

    A process for upgrading and stabilizing low-rank coal involving the sequential processing of the coal through three fluidized beds; first a dryer, then a pyrolyzer, and finally a cooler. The fluidizing gas for the cooler is the exit gas from the pyrolyzer with the addition of water for cooling. Overhead gas from pyrolyzing is likely burned to furnish the energy for the process. The product coal exits with a tar-like pitch sealant to enhance its safety during storage.

  9. Low-rank coal research

    SciTech Connect

    Weber, G. F.; Laudal, D. L.

    1989-01-01

    This work is a compilation of reports on ongoing research at the University of North Dakota. Topics include: Control Technology and Coal Preparation Research (SO{sub x}/NO{sub x} control, waste management), Advanced Research and Technology Development (turbine combustion phenomena, combustion inorganic transformation, coal/char reactivity, liquefaction reactivity of low-rank coals, gasification ash and slag characterization, fine particulate emissions), Combustion Research (fluidized bed combustion, beneficiation of low-rank coals, combustion characterization of low-rank coal fuels, diesel utilization of low-rank coals), Liquefaction Research (low-rank coal direct liquefaction), and Gasification Research (hydrogen production from low-rank coals, advanced wastewater treatment, mild gasification, color and residual COD removal from Synfuel wastewaters, Great Plains Gasification Plant, gasifier optimization).

  10. Process for clean-burning fuel from low-rank coal

    DOEpatents

    Merriam, N.W.; Sethi, V.; Brecher, L.E.

    1994-06-21

    A process is described for upgrading and stabilizing low-rank coal involving the sequential processing of the coal through three fluidized beds; first a dryer, then a pyrolyzer, and finally a cooler. The fluidizing gas for the cooler is the exit gas from the pyrolyzer with the addition of water for cooling. Overhead gas from pyrolyzing is likely burned to furnish the energy for the process. The product coal exits with a tar-like pitch sealant to enhance its safety during storage. 1 fig.

  11. Energy and environmental research emphasizing low-rank coal: Task 3.7, Fuel utilization properties

    SciTech Connect

    Zygarlicke, C.J.

    1995-08-01

    Gasification-type entrained ash and deposits were produced in a pressurized test furnace at high temperature. For the subbituminous Black Thunder coal, the effect of fuel-rich conditions was an increase in quartz, calcite, dolomite, and calcium-rich phases in the entrained ash. Lower particle temperatures, as compared to full air conventional combustion, and the oxygen-lean atmosphere may have caused a reduction in the interaction and assimilation of pure quartz and organically bound calcium into calcium aluminosilicate phases. For the Illinois No. 6 entrained fly ash fuel-rich conditions prevented the oxidation of pyrite and pyrrhotite to iron oxide. Lower temperatures within and surrounding char particles during reducing conditions combustion may have prevented the decomposition of pyrrhotite and enhanced the reaction of iron with aluminosilicate phases. The deposits show similar trends, with the Illinois No. 6 deposit grown under pressurized conditions at a lower temperature having Na and (Ca, Mg, Fe, Na, K) aluminosilicates, calcium carbonate, and an iron sulfide, probably pyrrohotite, present. At higher temperature, loss of sulfur occurs with the increased formation of iron aluminosilicate phases. The Illinois No. 6 and Black Thunder coals were tested with kaolin and lime additives under highly reducing conditions to simulate a gasification environment. The deposit collection zone temperature was varied from 750{degree}C to 1OOO{degree}C. Although no clear trends were evident for the interaction of kaolin or lime with the deposits, the deposits did become more porous, with greatly reduced strength shown for both additives.

  12. Two-in-one fuel combining sugar cane with low rank coal and its CO₂ reduction effects in pulverized-coal power plants.

    PubMed

    Lee, Dong-Wook; Bae, Jong-Soo; Lee, Young-Joo; Park, Se-Joon; Hong, Jai-Chang; Lee, Byoung-Hwa; Jeon, Chung-Hwan; Choi, Young-Chan

    2013-02-01

    Coal-fired power plants are facing to two major independent problems, namely, the burden to reduce CO(2) emission to comply with renewable portfolio standard (RPS) and cap-and-trade system, and the need to use low-rank coal due to the instability of high-rank coal supply. To address such unresolved issues, integrated gasification combined cycle (IGCC) with carbon capture and storage (CCS) has been suggested, and low rank coal has been upgraded by high-pressure and high-temperature processes. However, IGCC incurs huge construction costs, and the coal upgrading processes require fossil-fuel-derived additives and harsh operation condition. Here, we first show a hybrid coal that can solve these two problems simultaneously while using existing power plants. Hybrid coal is defined as a two-in-one fuel combining low rank coal with a sugar cane-derived bioliquid, such as molasses and sugar cane juice, by bioliquid diffusion into coal intrapores and precarbonization of the bioliquid. Unlike the simple blend of biomass and coal showing dual combustion behavior, hybrid coal provided a single coal combustion pattern. If hybrid coal (biomass/coal ratio = 28 wt %) is used as a fuel for 500 MW power generation, the net CO(2) emission is 21.2-33.1% and 12.5-25.7% lower than those for low rank coal and designed coal, and the required coal supply can be reduced by 33% compared with low rank coal. Considering high oil prices and time required before a stable renewable energy supply can be established, hybrid coal could be recognized as an innovative low-carbon-emission energy technology that can bridge the gulf between fossil fuels and renewable energy, because various water-soluble biomass could be used as an additive for hybrid coal through proper modification of preparation conditions. PMID:23286316

  13. Beyond Low Rank + Sparse: Multiscale Low Rank Matrix Decomposition

    NASA Astrophysics Data System (ADS)

    Ong, Frank; Lustig, Michael

    2016-06-01

    Low rank methods allow us to capture globally correlated components within matrices. The recent low rank + sparse decomposition further enables us to extract sparse entries along with the globally correlated components. In this paper, we present a natural generalization and consider the decomposition of matrices into components of multiple scales. Such decomposition is well motivated in practice as data matrices often exhibit local correlations in multiple scales. Concretely, we propose a multi-scale low rank modeling that represents a data matrix as a sum of block-wise low rank matrices with increasing scales of block sizes. We then consider the inverse problem of decomposing the data matrix into its multi-scale low rank components and approach the problem via a convex formulation. Theoretically, we show that under an incoherence condition, the convex program recovers the multi-scale low rank components exactly. Practically, we provide guidance on selecting the regularization parameters and incorporate cycle spinning to reduce blocking artifacts. Experimentally, we show that the multi-scale low rank decomposition provides a more intuitive decomposition than conventional low rank methods and demonstrate its effectiveness in four applications, including illumination normalization for face images, motion separation for surveillance videos, multi-scale modeling of the dynamic contrast enhanced magnetic resonance imaging and collaborative filtering exploiting age information.

  14. Vegetable oils as fuel alternatives - symposium overview

    SciTech Connect

    Pryde, E.H.

    1984-10-01

    Several encouraging statements can be made about the use of vegetable oil products as fuel as a result of the information presented in these symposium papers. Vegetable oil ester fuels have the greatest promise, but further engine endurance tests will be required. These can be carried out best by the engine manufacturers. Microemulsions appear to have promise, but more research and engine testing will be necessary before performance equivalent to the ester fuels can be developed. Such research effort can be justified because microemulsification is a rather uncomplicated physical process and might be adaptable to on-farm operations, which would be doubtful for the more involved transesterfication process. Although some answers have been provided by this symposium, others are still not available; engine testing is continuing throughout the world particularly in those countries that do not have access to petroleum. 9 references.

  15. Low-Rank Preserving Projections.

    PubMed

    Lu, Yuwu; Lai, Zhihui; Xu, Yong; Li, Xuelong; Zhang, David; Yuan, Chun

    2016-08-01

    As one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose its effectiveness. In this paper, it is assumed that data is grossly corrupted and the noise matrix is sparse. Based on these assumptions, we propose a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification. LRPP learns a low-rank weight matrix by projecting the data on a low-dimensional subspace. We use the L21 norm as a sparse constraint on the noise matrix and the nuclear norm as a low-rank constraint on the weight matrix. LRPP keeps the global structure of the data during the dimensionality reduction procedure and the learned low rank weight matrix can reduce the disturbance of noises in the data. LRPP can learn a robust subspace from the corrupted data. To verify the performance of LRPP in image dimensionality reduction and classification, we compare LRPP with the state-of-the-art dimensionality reduction methods. The experimental results show the effectiveness and the feasibility of the proposed method with encouraging results. PMID:26277014

  16. Third international symposium on alcohol fuels technology

    SciTech Connect

    1980-04-01

    At the opening of the Symposium, Dr. Sharrah, Senior Vice President of Continental Oil Company, addressed the attendees, and his remarks are included in this volume. The Symposium was concluded by workshops which addressed specific topics. The topical titles are as follows: alcohol uses; production; environment and safety; and socio-economic. The workshops reflected a growing confidence among the attendees that the alcohols from coal, remote natural gas and biomass do offer alternatives to petroleum fuels. Further, they may, in the long run, prove to be equal or superior to the petroleum fuels when the aspects of performance, environment, health and safety are combined with the renewable aspect of the biomass derived alcohols. Although considerable activity in the production and use of alcohols is now appearing in many parts of the world, the absence of strong, broad scale assessment and support for these fuels by the United States Federal Government was a noted point of concern by the attendees. The environmental consequence of using alcohols continues to be more benign in general than the petroleum based fuels. The exception is the family of aldehydes. Although the aldehydes are easily suppressed by catalysts, it is important to understand their production in the combustion process. Progress is being made in this regard. Of course, the goal is to burn the alcohols so cleanly that catalytic equipment can be eliminated. Separate abstracts are prepared for the Energy Data Base for individual presentations.

  17. Low-rank coal research. Quarterly report, January--March 1990

    SciTech Connect

    Not Available

    1990-08-01

    This document contains several quarterly progress reports for low-rank coal research that was performed from January-March 1990. Reports in Control Technology and Coal Preparation Research are in Flue Gas Cleanup, Waste Management, and Regional Energy Policy Program for the Northern Great Plains. Reports in Advanced Research and Technology Development are presented in Turbine Combustion Phenomena, Combustion Inorganic Transformation (two sections), Liquefaction Reactivity of Low-Rank Coals, Gasification Ash and Slag Characterization, and Coal Science. Reports in Combustion Research cover Fluidized-Bed Combustion, Beneficiation of Low-Rank Coals, Combustion Characterization of Low-Rank Coal Fuels, Diesel Utilization of Low-Rank Coals, and Produce and Characterize HWD (hot-water drying) Fuels for Heat Engine Applications. Liquefaction Research is reported in Low-Rank Coal Direct Liquefaction. Gasification Research progress is discussed for Production of Hydrogen and By-Products from Coal and for Chemistry of Sulfur Removal in Mild Gas.

  18. Low-rank coal oil agglomeration

    DOEpatents

    Knudson, Curtis L.; Timpe, Ronald C.

    1991-01-01

    A low-rank coal oil agglomeration process. High mineral content, a high ash content subbituminous coals are effectively agglomerated with a bridging oil which is partially water soluble and capable of entering the pore structure, and usually coal derived.

  19. Low-Rank Coal and Advanced Technologies for Power Generation

    NASA Astrophysics Data System (ADS)

    Zhang', Dong-ke; Jackson, Peter J.; Vuthaluru, Hari B.

    Fluidised-bed based advanced power generation technologies offer higher efficiencies than conventional pulverised fuel fired power plants and better prospects in reducing ash-related problems associated with low-rank coal in such plants. However, bed material agglomeration and bed defluidisation present significant operational difficulties for the utilisation of the low-rank coal in fluidised-bed processes. Alkali and alkaline-earth elements and sulphur compounds, often found in low-rank coals, form low melting point eutectics at typical fluidised-bed combustion and gasification operating temperatures. These low melting-point materials are subsequently transferred onto the bed material particle surfaces, and the ash-coated particles then become adhesive and agglomerate. Defluidisation can occur either as an extension of agglomeration as a rate process gradually leading to defluidisation or as an instantaneous event without agglomeration. A critical thickness of the ash coating layer on the particle surface exists, above which defluidisation occurs. This critical thickness decreases with an increase in bed temperature. Several mineral additives, alternative bed materials and pretreatment of coal have been shown to suppress, to different extents, particle agglomeration and bed defluidisation when burning a high sodium, high sulphur low-rank coal in a spouted fluidised-bed combustor. Sillimanite as an alternative bed material is found to be most effective for defluidisation control. Alternative advanced technologies such as low-temperature pyrolysis and co-production are proposed for future investigation.

  20. The 17th Symposium on Biotechnology for Fuels and Chemicals

    NASA Astrophysics Data System (ADS)

    This volume contains the abstracts of oral and poster presentations made at the Seventeenth Symposium on Biotechnology for Fuels and Chemicals. Session titles include Thermal, Chemical, and Biological Processing; Applied Biological Research; Bioprocessing Research; Special Topics Discussion Groups; Process Economics and Commercialization; and Environmental Biotechnology.

  1. SYMPOSIUM PROCEEDINGS: ENVIRONMENTAL ASPECTS OF FUEL CONVERSION TECHNOLOGY, IV (APRIL 1979, HOLLYWOOD, FL)

    EPA Science Inventory

    The proceedings document presentations made at the symposium on Environmental Aspects of Fuel Conversion Technology. The symposium acted as a colloquium for discussion of environmentally related information on coal gasification and liquefaction. The program included sessions on p...

  2. Adjoints and Low-rank Covariance Representation

    NASA Technical Reports Server (NTRS)

    Tippett, Michael K.; Cohn, Stephen E.

    2000-01-01

    Quantitative measures of the uncertainty of Earth System estimates can be as important as the estimates themselves. Second moments of estimation errors are described by the covariance matrix, whose direct calculation is impractical when the number of degrees of freedom of the system state is large. Ensemble and reduced-state approaches to prediction and data assimilation replace full estimation error covariance matrices by low-rank approximations. The appropriateness of such approximations depends on the spectrum of the full error covariance matrix, whose calculation is also often impractical. Here we examine the situation where the error covariance is a linear transformation of a forcing error covariance. We use operator norms and adjoints to relate the appropriateness of low-rank representations to the conditioning of this transformation. The analysis is used to investigate low-rank representations of the steady-state response to random forcing of an idealized discrete-time dynamical system.

  3. Low-rank coal oil agglomeration

    DOEpatents

    Knudson, C.L.; Timpe, R.C.

    1991-07-16

    A low-rank coal oil agglomeration process is described. High mineral content, a high ash content subbituminous coals are effectively agglomerated with a bridging oil which is partially water soluble and capable of entering the pore structure, and is usually coal-derived.

  4. Low-rank coal oil agglomeration product and process

    DOEpatents

    Knudson, C.L.; Timpe, R.C.; Potas, T.A.; DeWall, R.A.; Musich, M.A.

    1992-11-10

    A selectively-sized, raw, low-rank coal is processed to produce a low ash and relative water-free agglomerate with an enhanced heating value and a hardness sufficient to produce a non-degradable, shippable fuel. The low-rank coal is treated, under high shear conditions, in the first stage to cause ash reduction and subsequent surface modification which is necessary to facilitate agglomerate formation. In the second stage the treated low-rank coal is contacted with bridging and binding oils under low shear conditions to produce agglomerates of selected size. The bridging and binding oils may be coal or petroleum derived. The process incorporates a thermal deoiling step whereby the bridging oil may be completely or partially recovered from the agglomerate; whereas, partial recovery of the bridging oil functions to leave as an agglomerate binder, the heavy constituents of the bridging oil. The recovered oil is suitable for recycling to the agglomeration step or can serve as a value-added product.

  5. Low-rank coal oil agglomeration product and process

    DOEpatents

    Knudson, Curtis L.; Timpe, Ronald C.; Potas, Todd A.; DeWall, Raymond A.; Musich, Mark A.

    1992-01-01

    A selectively-sized, raw, low-rank coal is processed to produce a low ash and relative water-free agglomerate with an enhanced heating value and a hardness sufficient to produce a non-decrepitating, shippable fuel. The low-rank coal is treated, under high shear conditions, in the first stage to cause ash reduction and subsequent surface modification which is necessary to facilitate agglomerate formation. In the second stage the treated low-rank coal is contacted with bridging and binding oils under low shear conditions to produce agglomerates of selected size. The bridging and binding oils may be coal or petroleum derived. The process incorporates a thermal deoiling step whereby the bridging oil may be completely or partially recovered from the agglomerate; whereas, partial recovery of the bridging oil functions to leave as an agglomerate binder, the heavy constituents of the bridging oil. The recovered oil is suitable for recycling to the agglomeration step or can serve as a value-added product.

  6. A low rank approach to automatic differentiation.

    SciTech Connect

    Abdel-Khalik, H. S.; Hovland, P. D.; Lyons, A.; Stover, T. E.; Utke, J.; Mathematics and Computer Science; North Carolina State Univ.; Univ. of Chicago

    2008-01-01

    This manuscript introduces a new approach for increasing the efficiency of automatic differentiation (AD) computations for estimating the first order derivatives comprising the Jacobian matrix of a complex large-scale computational model. The objective is to approximate the entire Jacobian matrix with minimized computational and storage resources. This is achieved by finding low rank approximations to a Jacobian matrix via the Efficient Subspace Method (ESM). Low rank Jacobian matrices arise in many of today's important scientific and engineering problems, e.g. nuclear reactor calculations, weather climate modeling, geophysical applications, etc. A low rank approximation replaces the original Jacobian matrix J (whose size is dictated by the size of the input and output data streams) with matrices of much smaller dimensions (determined by the numerical rank of the Jacobian matrix). This process reveals the rank of the Jacobian matrix and can be obtained by ESM via a series of r randomized matrix-vector products of the form: Jq, and J{sup T} {omega} which can be evaluated by the AD forward and reverse modes, respectively.

  7. Combustion behavior of low rank coal water slurries

    SciTech Connect

    Yavuz, R.; Kuecuekbayrak, S.; Williams, A.

    1996-12-31

    Coal water slurries have been developed over the last 15 years as an alternative to fuel oil mainly in industry and power station boilers. Observing of droplet lifetime reveals details of the mechanism of the slurry combustion. In the present investigation, single droplet combustion of lignite water slurries using different Turkish lignites were experimentally studied by using single droplet combustion technique. The technique is based on thermometric method. Results of combustion behavior of low rank coal water slurries were compared with that of high rank coal water slurries which were found in the literature.

  8. Robust Generalized Low Rank Approximations of Matrices

    PubMed Central

    Shi, Jiarong; Yang, Wei; Zheng, Xiuyun

    2015-01-01

    In recent years, the intrinsic low rank structure of some datasets has been extensively exploited to reduce dimensionality, remove noise and complete the missing entries. As a well-known technique for dimensionality reduction and data compression, Generalized Low Rank Approximations of Matrices (GLRAM) claims its superiority on computation time and compression ratio over the SVD. However, GLRAM is very sensitive to sparse large noise or outliers and its robust version does not have been explored or solved yet. To address this problem, this paper proposes a robust method for GLRAM, named Robust GLRAM (RGLRAM). We first formulate RGLRAM as an l1-norm optimization problem which minimizes the l1-norm of the approximation errors. Secondly, we apply the technique of Augmented Lagrange Multipliers (ALM) to solve this l1-norm minimization problem and derive a corresponding iterative scheme. Then the weak convergence of the proposed algorithm is discussed under mild conditions. Next, we investigate a special case of RGLRAM and extend RGLRAM to a general tensor case. Finally, the extensive experiments on synthetic data show that it is possible for RGLRAM to exactly recover both the low rank and the sparse components while it may be difficult for previous state-of-the-art algorithms. We also discuss three issues on RGLRAM: the sensitivity to initialization, the generalization ability and the relationship between the running time and the size/number of matrices. Moreover, the experimental results on images of faces with large corruptions illustrate that RGLRAM obtains the best denoising and compression performance than other methods. PMID:26367116

  9. Anaerobic bioprocessing of low rank coals

    SciTech Connect

    Jain, M.K.; Narayan, R.; Han, O.

    1991-01-01

    The overall goal of this project is to find biological methods to remove carboxylic functionalities from low rank coals under ambient conditions and to assess the properties of these modified coals towards coal liquefaction. The main objectives for this quarter were: (1) enrichment of anaerobic microbial consortia in a coal fed chemostat, (2) characterization of biocoal products and examination of liquefaction potential, (3) isolation of decarboxylating organisms and evaluation of the isolated organisms for decarboxylation. The project began on September 12, 1990. 3 figs., 7 tabs.

  10. CO2 Sequestration Potential of Texas Low-Rank Coals

    SciTech Connect

    Duane A. McVay; Walter B. Ayers Jr; Jerry L. Jensen

    2006-03-01

    The objectives of this project are to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (ECBM) recovery as an added benefit of sequestration. In this reporting period we revised all of the economic calculations, participated in technology transfer of project results, and began working on project closeout tasks in anticipation of the project ending December 31, 2005. In this research, we conducted five separate simulation investigations, or cases. These cases are (1) CO{sub 2} sequestration base case scenarios for 4,000-ft and 6,200-ft depth coal beds in the Lower Calvert Bluff Formation of east-central Texas, (2) sensitivity study of the effects of well spacing on sequestration, (3) sensitivity study of the effects of injection gas composition, (4) sensitivity study of the effects of injection rate, and (5) sensitivity study of the effects of coal dewatering prior to CO{sub 2} injection/sequestration. Results show that, in most cases, revenue from coalbed methane production does not completely offset the costs of CO{sub 2} sequestration in Texas low-rank coals, indicating that CO{sub 2} injection is not economically feasible for the ranges of gas prices and carbon credits investigated. The best economic performance is obtained with flue gas (13% CO{sub 2} - 87% N{sub 2}) injection, as compared to injection of 100% CO{sub 2} and a mixture of 50% CO{sub 2} and 50% N{sub 2}. As part of technology transfer for this project, we presented results at the West Texas Geological Society Fall Symposium in October 2005 and at the COAL-SEQ Forum in November 2005.

  11. CO2 Sequestration Potential of Texas Low-Rank Coals

    SciTech Connect

    Duane A. McVay; Walter B. Ayers, Jr; Jerry L. Jensen

    2006-05-01

    The objectives of this project are to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (ECBM) recovery as an added benefit of sequestration. The main objectives for this reporting period were to (1) determine the effects of permeability anisotropy on performance of CO{sub 2} sequestration and ECBM production in the Lower Calvert Bluff Formation (LCB) of the Wilcox Group coals in east-central Texas, and (2) begin reservoir and economic analyses of CO{sub 2} sequestration and ECBM production using horizontal wells. To evaluate the effects of permeability anisotropy on CO{sub 2} sequestration and ECBM in LCB coal beds, we conducted deterministic reservoir modeling studies of 100% CO{sub 2} gas injection for the 6,200-ft depth base case (Case 1b) using the most likely values of the reservoir parameters. Simulation results show significant differences in the cumulative volumes of CH{sub 4} produced and CO{sub 2} injected due to permeability anisotropy, depending on the orientation of injection patterns relative to the orientation of permeability anisotropy. This indicates that knowledge of the magnitude and orientation of permeability anisotropy will be an important consideration in the design of CO{sub 2} sequestration and ECBM projects. We continued discussions with Anadarko Petroleum regarding plans for additional coal core acquisition and laboratory work to further characterize Wilcox low-rank coals. As part of the technology transfer for this project, we submitted the paper SPE 100584 for presentation at the 2006 SPE Gas Technology Symposium to be held in Calgary, Alberta, Canada on May 15-18, 2006.

  12. CO2 Sequestration Potential of Texas Low-Rank Coals

    SciTech Connect

    Duane A. McVay; Walter B. Ayers Jr; Jerry L. Jensen

    2006-07-01

    The objectives of this project are to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (ECBM) recovery as an added benefit of sequestration. The main objectives for this reporting period were to (1) estimate the potential for CO{sub 2} sequestration in, and methane production from, low-rank coals of the Lower Calvert Bluff Formation of the Wilcox Group in the east-central Texas region, (2) quantify uncertainty associated with these estimates, (3) conduct reservoir and economic analyses of CO{sub 2} sequestration and ECBM production using horizontal wells, and (4) compare the results with those obtained from previous studies of vertical wells. To estimate the total volumes of CO{sub 2} that may be sequestered in, and total volumes of methane that can be produced from, the Wilcox Group low-rank coals in east-central Texas, we used data provided by Anadarko Petroleum Corporation, data obtained during this research, and results of probabilistic simulation modeling studies we conducted. For the analysis, we applied our base-case coal seam characteristics to a 2,930-mi{sup 2} (1,875,200-ac) area where Calvert Bluff coal seams range between 4,000 and 6,200 ft deep. Results of the probabilistic analysis indicate that potential CO{sub 2} sequestration capacity of the coals ranges between 27.2 and 49.2 Tcf (1.57 and 2.69 billion tons), with a mean value of 38 Tcf (2.2 billion tons), assuming a 72.4% injection efficiency. Estimates of recoverable methane resources, assuming a 71.3% recovery factor, range between 6.3 and 13.6 Tcf, with a mean of 9.8 Tcf. As part of the technology transfer for this project, we presented the paper SPE 100584 at the 2006 SPE Gas Technology Symposium held in Calgary, Alberta, Canada, on May 15-18, 2006. Also, we submitted an abstract to be considered for inclusion in a special volume dedicated to CO{sub 2} sequestration in geologic media, which

  13. Low-rank coal research, Task 5.1. Topical report, April 1986--December 1992

    SciTech Connect

    Not Available

    1993-02-01

    This document is a topical progress report for Low-Rank Coal Research performed April 1986 - December 1992. Control Technology and Coal Preparation Research is described for Flue Gas Cleanup, Waste Management, Regional Energy Policy Program for the Northern Great Plains, and Hot-Gas Cleanup. Advanced Research and Technology Development was conducted on Turbine Combustion Phenomena, Combustion Inorganic Transformation (two sections), Liquefaction Reactivity of Low-Rank Coals, Gasification Ash and Slag Characterization, and Coal Science. Combustion Research is described for Atmospheric Fluidized-Bed Combustion, Beneficiation of Low-Rank Coals, Combustion Characterization of Low-Rank Fuels (completed 10/31/90), Diesel Utilization of Low-Rank Coals (completed 12/31/90), Produce and Characterize HWD (hot-water drying) Fuels for Heat Engine Applications (completed 10/31/90), Nitrous Oxide Emission, and Pressurized Fluidized-Bed Combustion. Liquefaction Research in Low-Rank Coal Direct Liquefaction is discussed. Gasification Research was conducted in Production of Hydrogen and By-Products from Coals and in Sulfur Forms in Coal.

  14. Bayes method for low rank tensor estimation

    NASA Astrophysics Data System (ADS)

    Suzuki, Taiji; Kanagawa, Heishiro

    2016-03-01

    We investigate the statistical convergence rate of a Bayesian low-rank tensor estimator, and construct a Bayesian nonlinear tensor estimator. The problem setting is the regression problem where the regression coefficient forms a tensor structure. This problem setting occurs in many practical applications, such as collaborative filtering, multi-task learning, and spatio-temporal data analysis. The convergence rate of the Bayes tensor estimator is analyzed in terms of both in-sample and out-of-sample predictive accuracies. It is shown that a fast learning rate is achieved without any strong convexity of the observation. Moreover, we extend the tensor estimator to a nonlinear function estimator so that we estimate a function that is a tensor product of several functions.

  15. Anaerobic processing of low-rank coals

    SciTech Connect

    Jain, M.K.; Narayan, R.; Han, O.

    1992-01-01

    The overall goal of this project is to find biological methods to remove carboxylic functionalities from low-rank coals and to assess the properties of the modified coal towards coal liquefaction. The main objectives for this quarter were: (i) continuation of microbial consortia maintenance and completion of coal decarboxylation using batch reactor system, (ii) decarboxylation of model polymer, (iii) characterization of biotreated coals, and (iv) microautoclave liquefaction of the botreated coal. Progress is reported on the thermogravimetric analysis of coal biotreated in the absence of methanogens and under 5% hydrogen gas exhibits increased volatile carbon to fixed carbon ratio; that the microbial consortia developed on coal are being adapted to two different model polymers containing free carboxylic groups to examine decarboxylation ability of consortium; completion of experiments to decarboxylate two model polymers, polyacrylic acid and polymethyl methacrylate, have been completed; that the biotreated coal showed increase in THF-solubles.

  16. Anaerobic bioprocessing of low-rank coals

    SciTech Connect

    Jain, M.K.; Narayan, R.; Han, O.

    1992-07-14

    We are seeking to find biological methods to remove carboxylic functionalities from low-rank coals and to assess the properties of the modified coal towards coal liquefaction. The main objectives for this quarter were : continuation of microbial consortia development and maintenance, evaluation of commercial decarboxylase, decarboxylation of lignite, demineralized Wyodak coal and model polymer, and characterization of biotreated coals. Specifically we report that two batch fermentor systems were completed and three other fermentors under optimum conditions for coal decarboxylation are in progress; that inhibition of growth of methanogens in the batch fermentor system enhanced the carbon dioxide production; that adapted microbial consortium produced more gas from lignite than Wyodak subbituminous coal; that phenylalanine decarboxylase exhibited insignificant coal decarboxylation activity; that two different microbial consortia developed on coal seem to be effective in decarboxylation of a polymer containing free carboxylic groups; and that CHN analyses of additional biotreated coals reconfirm increase in H/C ratio by 3--6%.

  17. Low-rank coal study. Volume 4. Regulatory, environmental, and market analyses

    SciTech Connect

    Not Available

    1980-11-01

    The regulatory, environmental, and market constraints to development of US low-rank coal resources are analyzed. Government-imposed environmental and regulatory requirements are among the most important factors that determine the markets for low-rank coal and the technology used in the extraction, delivery, and utilization systems. Both state and federal controls are examined, in light of available data on impacts and effluents associated with major low-rank coal development efforts. The market analysis examines both the penetration of existing markets by low-rank coal and the evolution of potential markets in the future. The electric utility industry consumes about 99 percent of the total low-rank coal production. This use in utility boilers rose dramatically in the 1970's and is expected to continue to grow rapidly. In the late 1980's and 1990's, industrial direct use of low-rank coal and the production of synthetic fuels are expected to start growing as major new markets.

  18. SYMPOSIUM PROCEEDINGS: ENVIRONMENTAL ASPECTS OF FUEL CONVERSION TECHNOLOGY, V (SEPTEMBER 1980, ST. LOUIS, MO)

    EPA Science Inventory

    The report documents presentations at the fifth EPA-sponsored symposium on the environmental aspects of fuel conversion technology, in St. Louis, MO, 9/16-19/80. The symposium served as a colloquium on environmental information related to coal gasification, indirect liquefaction,...

  19. Eighteenth symposium on biotechnology for fuels and chemicals: Program and abstracts

    SciTech Connect

    1996-12-31

    This volume provides the proceedings for the Eighteenth Symposium on Biotechnology for Fuels and Chemicals held May 5-9, 1996 in Gatlinburg, Tennessee. The proceedings contains abstracts for oral and poster presentations.

  20. Process to improve boiler operation by supplemental firing with thermally beneficiated low rank coal

    DOEpatents

    Sheldon, Ray W.

    2001-01-01

    The invention described is a process for improving the performance of a commercial coal or lignite fired boiler system by supplementing its normal coal supply with a controlled quantity of thermally beneficiated low rank coal, (TBLRC). This supplemental TBLRC can be delivered either to the solid fuel mill (pulverizer) or directly to the coal burner feed pipe. Specific benefits are supplied based on knowledge of equipment types that may be employed on a commercial scale to complete the process. The thermally beneficiated low rank coal can be delivered along with regular coal or intermittently with regular coal as the needs require.

  1. Chemical comminution and deashing of low-rank coals

    DOEpatents

    Quigley, David R.

    1992-01-01

    A method of chemically comminuting a low-rank coal while at the same time increasing the heating value of the coal. A strong alkali solution is added to a low-rank coal to solubilize the carbonaceous portion of the coal, leaving behind the noncarbonaceous mineral matter portion. The solubilized coal is precipitated from solution by a multivalent cation, preferably calcium.

  2. Chemical comminution and deashing of low-rank coals

    DOEpatents

    Quigley, David R.

    1992-12-01

    A method of chemically comminuting a low-rank coal while at the same time increasing the heating value of the coal. A strong alkali solution is added to a low-rank coal to solubilize the carbonaceous portion of the coal, leaving behind the noncarbonaceous mineral matter portion. The solubilized coal is precipitated from solution by a multivalent cation, preferably calcium.

  3. Upgrading low rank coal using the Koppelman Series C process

    SciTech Connect

    Merriam, N.W., Western Research Institute

    1998-01-01

    Development of the K-Fuel technology began after the energy shortage of the early 1970s in the United States led energy producers to develop the huge deposits of low-sulfur coal in the Powder River Basin (PRB) of Wyoming. PRB coal is a subbituminous C coal containing about 30 wt % moisture and having heating values of about 18.6 megajoules/kg (8150 Btu/lb). PRB coal contains from 0.3 to 0.5 wt % sulfur, which is nearly all combined with the organic matrix in the coal. It is in much demand for boiler fuel because of the low-sulfur content and the low price. However, the low-heating value limits the markets for PRB coal to boilers specially designed for the high- moisture coal. Thus, the advantages of the low-sulfur content are not available to many potential customers having boilers that were designed for bituminous coal. This year about 250 million tons of coal is shipped from the Powder River Basin of Wyoming. The high- moisture content and, consequently, the low-heating value of this coal causes the transportation and combustion of the coal to be inefficient. When the moisture is removed and the heating value increased the same bundle of energy can be shipped using one- third less train loads. Also, the dried product can be burned much more efficiently in boiler systems. This increase in efficiency reduces the carbon dioxide emissions caused by use of the low-heating value coal. Also, the processing used to remove water and restructure the coal removes sulfur, nitrogen, mercury, and chlorides from the coal. This precombustion cleaning is much less costly than stack scrubbing. PRB coal, and other low-rank coals, tend to be highly reactive when freshly mined. These reactive coals must be mixed regularly (every week or two) when fresh, but become somewhat more stable after they have aged for several weeks. PRB coal is relatively dusty and subject to self-ignition compared to bituminous coals. When dried using conventional technology, PRB coal is even more dusty and

  4. Addendum: Tenth International Symposium on Alcohol Fuels, The road to commercialization

    SciTech Connect

    Not Available

    1994-05-01

    The Tenth International Symposium on ALCOHOL FUELS ``THE ROAD TO COMMERCIALIZATION`` was held at the Broadmoor Hotel, Colorado Springs, Colorado, USA November 7--10, 1993. Twenty-seven papers on the production of alcohol fuels, specifications, their use in automobiles, buses and trucks, emission control, and government policies were presented. Individual papers have been processed separately for entry into the data base.

  5. Low-rank coal research. Final technical report, April 1, 1988--June 30, 1989, including quarterly report, April--June 1989

    SciTech Connect

    Not Available

    1989-12-31

    This work is a compilation of reports on ongoing research at the University of North Dakota. Topics include: Control Technology and Coal Preparation Research (SO{sub x}/NO{sub x} control, waste management), Advanced Research and Technology Development (turbine combustion phenomena, combustion inorganic transformation, coal/char reactivity, liquefaction reactivity of low-rank coals, gasification ash and slag characterization, fine particulate emissions), Combustion Research (fluidized bed combustion, beneficiation of low-rank coals, combustion characterization of low-rank coal fuels, diesel utilization of low-rank coals), Liquefaction Research (low-rank coal direct liquefaction), and Gasification Research (hydrogen production from low-rank coals, advanced wastewater treatment, mild gasification, color and residual COD removal from Synfuel wastewaters, Great Plains Gasification Plant, gasifier optimization).

  6. Factors affecting quality of dried low-rank coals

    SciTech Connect

    Karthikeyan, M.; Kuma, J.V.M.; Hoe, C.S.; Ngo, D.L.Y.

    2007-07-01

    The chemical and physical properties of coal are strongly affected by the upgrading process employed. For high-moisture coals, upgrading involves thermal dehydration to improve the calorific value of the coal on mass basis. This study evaluates the feasibility of upgrading a low-rank/grade coal using the oven drying method. The objective of this research work is to study the drying characteristics of low-rank coals and to understand the factors affecting the quality of dried low-rank coals. This article describes laboratory experiments conducted on the characterization of the low-rank coals before and after the drying process. The results on drying kinetics, re-absorption of coal samples, and proximate analysis of coal samples before and after drying are discussed. It was found that the upgrading process produced coal with better heating value and combustion characteristics than those of the raw coal samples.

  7. Structure, constitution and utilization of low rank Indian coal

    SciTech Connect

    Iyengar, M.S.; Iyengar, V.A.

    1996-12-31

    This paper briefly reviews the work done on lignite and sub-bituminous coals. Surface area and moisture adsorption dependency on functional group is described. The role of hydrogen bonding in the briquetting of lignite and of alkyl groups in inducing caking properties are discussed. The dualistic behavior of abnormal coals as both a low and high rank coal is also discussed in relation to the nature of their sulphur groups. On the utilization side, processes are described for: (1) Utilization of non-caking coal in the reduction of iron ore. Coal is first briquetted using a lime-tar binder. It is then carbonized for reducing iron ore. The bar is recovered and recycled. (2) Production of carbon black from low rank coals. In this process, coal is carbonized at high temperature in a fluidized bed. Carbon black, for tire industry, is obtained with char as by-product. (3) Utilization of flue gases of industry is also discussed. In this new approach, the flue gas is reduced to synthesis gas by additional fuel and the inevitable surplus heat. The viability of the process is illustrated by details of a recent study in a cement plant. In addition to the above, the implication of recycling flue gas in automobile engines to make them more environment friendly and cost effective, is also discussed.

  8. Relations Among Some Low-Rank Subspace Recovery Models.

    PubMed

    Zhang, Hongyang; Lin, Zhouchen; Zhang, Chao; Gao, Junbin

    2015-09-01

    Recovering intrinsic low-dimensional subspaces from data distributed on them is a key preprocessing step to many applications. In recent years, a lot of work has modeled subspace recovery as low-rank minimization problems. We find that some representative models, such as robust principal component analysis (R-PCA), robust low-rank representation (R-LRR), and robust latent low-rank representation (R-LatLRR), are actually deeply connected. More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations. Since R-PCA is the simplest, our discovery makes it the center of low-rank subspace recovery models. Our work has two important implications. First, R-PCA has a solid theoretical foundation. Under certain conditions, we could find globally optimal solutions to these low-rank models at an overwhelming probability, although these models are nonconvex. Second, we can obtain significantly faster algorithms for these models by solving R-PCA first. The computation cost can be further cut by applying low-complexity randomized algorithms, for example, our novel l2,1 filtering algorithm, to R-PCA. Although for the moment the formal proof of our l2,1 filtering algorithm is not yet available, experiments verify the advantages of our algorithm over other state-of-the-art methods based on the alternating direction method. PMID:26161818

  9. Seventeenth symposium on biotechnology for fuels and chemicals. Program and abstracts

    SciTech Connect

    1995-05-01

    This volume contains the abstracts of oral and poster presentations made at the Seventeenth Symposium on Biotechnology for Fuels and Chemicals. Session titles include Thermal, Chemical, and Biological Processing; Applied Biological Research; Bioprocessing Research; Special Topics Discussion Groups; Process Economics and Commercialization; and Environmental Biotechnology.

  10. Twelfth symposium on biotechnology for fuels and chemicals: Program and abstracts

    SciTech Connect

    Scheitlin, F.M.

    1990-01-01

    This report is the program and abstracts of the twelfth symposium on biotechnology for fuels and chemicals, held on May 7--11, 1990, at Gatlinburg, Tennessee. The symposium, sponsored by the Department of Energy, Oak Ridge National Laboratory, Solar Energy Research Institute, Badger Engineers, Inc., Gas Research Institute, and American Chemical Society, consists of five sessions: Session 1, thermal, chemical, and biological processing; Session 2 and 3, applied biological research; Session 4, bioengineering research; and Session 5, biotechnology, bioengineering, and the solution of environmental problems. It also consists of a poster session of the same five subject categories.

  11. International symposium on fuel rod simulators: development and application

    SciTech Connect

    McCulloch, R.W.

    1981-05-01

    Separate abstracts are included for each of the papers presented concerning fuel rod simulator operation and performance; simulator design and evaluation; clad heated fuel rod simulators and fuel rod simulators for cladding investigations; fuel rod simulator components and inspection; and simulator analytical modeling. Ten papers have previously been input to the Energy Data Base.

  12. Low-Rank Total Variation for Image Super-Resolution

    PubMed Central

    Shi, Feng; Cheng, Jian; Wang, Li; Yap, Pew-Thian; Shen, Dinggang

    2014-01-01

    Most natural images can be approximated using their low-rank components. This fact has been successfully exploited in recent advancements of matrix completion algorithms for image recovery. However, a major limitation of low-rank matrix completion algorithms is that they cannot recover the case where a whole row or column is missing. The missing row or column will be simply filled as an arbitrary combination of other rows or columns with known values. This precludes the application of matrix completion to problems such as super-resolution (SR) where missing values in many rows and columns need to be recovered in the process of up-sampling a low-resolution image. Moreover, low-rank regularization considers information globally from the whole image and does not take proper consideration of local spatial consistency. Accordingly, we propose in this paper a solution to the SR problem via simultaneous (global) low-rank and (local) total variation (TV) regularization. We solve the respective cost function using the alternating direction method of multipliers (ADMM). Experiments on MR images of adults and pediatric subjects demonstrate that the proposed method enhances the details of the recovered high-resolution images, and outperforms the nearest-neighbor interpolation, cubic interpolation, non-local means, and TV-based up-sampling. PMID:24505661

  13. Stabilized thermally beneficiated low rank coal and method of manufacture

    SciTech Connect

    Viall, A.J.; Richards, J.M.

    2000-07-18

    A process is described for reducing the spontaneous combustion tendencies of thermally beneficiated low rank coals employing heat, air or an oxygen containing gas followed by an optional moisture addition. Specific reaction conditions are supplied along with knowledge of equipment types that may be employed on a commercial scale to complete the process.

  14. Stabilized thermally beneficiated low rank coal and method of manufacture

    DOEpatents

    Viall, Arthur J.; Richards, Jeff M.

    2000-01-01

    A process for reducing the spontaneous combustion tendencies of thermally beneficiated low rank coals employing heat, air or an oxygen containing gas followed by an optional moisture addition. Specific reaction conditions are supplied along with knowledge of equipment types that may be employed on a commercial scale to complete the process.

  15. Stabilized thermally beneficiated low rank coal and method of manufacture

    DOEpatents

    Viall, A.J.; Richards, J.M.

    1999-01-26

    A process is described for reducing the spontaneous combustion tendencies of thermally beneficiated low rank coals employing heat, air or an oxygen containing gas followed by an optional moisture addition. Specific reaction conditions are supplied along with knowledge of equipment types that may be employed on a commercial scale to complete the process. 3 figs.

  16. Stabilized thermally beneficiated low rank coal and method of manufacture

    DOEpatents

    Viall, Arthur J.; Richards, Jeff M.

    1999-01-01

    A process for reducing the spontaneous combustion tendencies of thermally beneficiated low rank coals employing heat, air or an oxygen containing gas followed by an optional moisture addition. Specific reaction conditions are supplied along with knowledge of equipment types that may be employed on a commercial scale to complete the process.

  17. AFBC bed material performance with low-rank coals

    SciTech Connect

    Goblirsch, G.M.; Benson, S.A.; Karner, F.R.; Rindt, D.K.; Hajicek, D.R.

    1983-01-01

    The purpose of this paper is to describe the reasons for carefully screening any candidate bed material for use in low-rank coal atmospheric fluidized-bed combustion, before the final selection is made. The sections of this paper describe: (1) the experimental equipment used to obtain the data, as well as the experimental and analytical procedures used in evaluation; (2) the results of tests utilizing various bed materials with particular emphasis on the problem of bed material agglomeration; and (3) the conclusions and recommendations for bed material selection and control for use with low-rank coal. Bed materials of aluminum oxide, quartz, limestone, dolomite, granite, gabbro, and mixtures of some of these materials have been used in the testing. Of these materials, gabbro appears most suitable for use with high available sodium lignites. 17 figures, 8 tables. (DMC)

  18. Introduction to the proceedings of the sixteenth symposium on biotechnology for fuels and chemicals

    SciTech Connect

    Davison, B.H.

    1994-12-31

    Biotechnology can be defined as the use of biologically derived materials and biocatalysts to carry out desired transformations from one material to another. These biocatalysts can be enzymes or microorganisms. The transformation may be of raw materials into useful compounds or for the destruction of industrial wastes. One use of biotechnology is for the production of fuels and chemicals. This has been the broad area focused on by this Symposium for the past 16 years. The Symposium on Biotechnology for Fuels and Chemicals presents both applied and fundamental work in this area performed by universities, industries, and government institutions. The goal, whether near term or long term, is to find and demonstrate efficient, economical methods for the use of biotechnology to supply society`s needs for fuels and chemicals. The Symposium allows interactions among the researchers in an intimate setting to foster the interactions that will be necessary to commercialize and use these technologies. Efforts presented include all aspects of the process: the pretreatment and beneficiation of the raw material, the biological conversion in some reactor, the separation and recovery of the desired product, and the treatment of the waste streams from this and earlier legacy processes. There are also efforts of the sensing, monitoring, and control of the process and well and the economic analysis to estimate the overall utility and impact. The Sixteenth Symposium on Biotechnology for Fuels and Chemicals provided a forum for the exchange of ideas. There were 34 oral presentations and 81 poster presentations. These were organized into sessions of thermal, chemical, and biological processing; bioprocessing research; process economics and commercialization; and environmental biotechnology.

  19. Low-Rank Regularization for Learning Gene Expression Programs

    PubMed Central

    Ye, Guibo; Tang, Mengfan; Cai, Jian-Feng; Nie, Qing; Xie, Xiaohui

    2013-01-01

    Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets. PMID:24358148

  20. Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging

    PubMed Central

    Yu, Xingjian; Chen, Shuhang; Hu, Zhenghui; Liu, Meng; Chen, Yunmei; Shi, Pengcheng; Liu, Huafeng

    2015-01-01

    In dynamic Positron Emission Tomography (PET), an estimate of the radio activity concentration is obtained from a series of frames of sinogram data taken at ranging in duration from 10 seconds to minutes under some criteria. So far, all the well-known reconstruction algorithms require known data statistical properties. It limits the speed of data acquisition, besides, it is unable to afford the separated information about the structure and the variation of shape and rate of metabolism which play a major role in improving the visualization of contrast for some requirement of the diagnosing in application. This paper presents a novel low rank-based activity map reconstruction scheme from emission sinograms of dynamic PET, termed as SLCR representing Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging. In this method, the stationary background is formulated as a low rank component while variations between successive frames are abstracted to the sparse. The resulting nuclear norm and l1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods. In this paper, the linearized alternating direction method is applied. The effectiveness of the proposed scheme is illustrated on three data sets. PMID:26540274

  1. Introduction and session summaries for the proceedings of the twelfth symposium on biotechnology fuels and chemicals

    SciTech Connect

    Greenbaum, E. ); Wyman, C.E. )

    1990-01-01

    This Twelfth Symposium on Biotechnology for Fuels and Chemicals continues to provide an annual forum for researchers from industry, universities, and government laboratories to exchange information on recent developments in emerging bioprocessing technologies. As in the past, innovative processing concepts are stressed that are in the early stages of development. The meeting began with a session on Thermal, Chemical, and Biological Processing, followed by two sessions on Applied Biological Research. Next, topics in Bioengineering Research were presented, and a special session on Biotechnology, Bioengineering, and the Solution of Environmental Problems concluded the Twelfth Symposium. Both presentations and posters provided information exchange among meeting participants, and several discussion groups were organized to consider special topics of interest to the meeting participants. This paper presents a brief description of the discussions.

  2. DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS.

    PubMed

    Schuler, Alejandro; Liu, Vincent; Wan, Joe; Callahan, Alison; Udell, Madeleine; Stark, David E; Shah, Nigam H

    2016-01-01

    The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that low rank modeling successfully captures known and putative phenotypes in these vastly different datasets. PMID:26776181

  3. Low-Rank Coal Grinding Performance Versus Power Plant Performance

    SciTech Connect

    Rajive Ganguli; Sukumar Bandopadhyay

    2008-12-31

    The intent of this project was to demonstrate that Alaskan low-rank coal, which is high in volatile content, need not be ground as fine as bituminous coal (typically low in volatile content) for optimum combustion in power plants. The grind or particle size distribution (PSD), which is quantified by percentage of pulverized coal passing 74 microns (200 mesh), affects the pulverizer throughput in power plants. The finer the grind, the lower the throughput. For a power plant to maintain combustion levels, throughput needs to be high. The problem of particle size is compounded for Alaskan coal since it has a low Hardgrove grindability index (HGI); that is, it is difficult to grind. If the thesis of this project is demonstrated, then Alaskan coal need not be ground to the industry standard, thereby alleviating somewhat the low HGI issue (and, hopefully, furthering the salability of Alaskan coal). This project studied the relationship between PSD and power plant efficiency, emissions, and mill power consumption for low-rank high-volatile-content Alaskan coal. The emissions studied were CO, CO{sub 2}, NO{sub x}, SO{sub 2}, and Hg (only two tests). The tested PSD range was 42 to 81 percent passing 76 microns. Within the tested range, there was very little correlation between PSD and power plant efficiency, CO, NO{sub x}, and SO{sub 2}. Hg emissions were very low and, therefore, did not allow comparison between grind sizes. Mill power consumption was lower for coarser grinds.

  4. Low rank approximation in G 0 W 0 calculations

    NASA Astrophysics Data System (ADS)

    Shao, MeiYue; Lin, Lin; Yang, Chao; Liu, Fang; Da Jornada, Felipe H.; Deslippe, Jack; Louie, Steven G.

    2016-08-01

    The single particle energies obtained in a Kohn--Sham density functional theory (DFT) calculation are generally known to be poor approximations to electron excitation energies that are measured in transport, tunneling and spectroscopic experiments such as photo-emission spectroscopy. The correction to these energies can be obtained from the poles of a single particle Green's function derived from a many-body perturbation theory. From a computational perspective, the accuracy and efficiency of such an approach depends on how a self energy term that properly accounts for dynamic screening of electrons is approximated. The $G_0W_0$ approximation is a widely used technique in which the self energy is expressed as the convolution of a non-interacting Green's function ($G_0$) and a screened Coulomb interaction ($W_0$) in the frequency domain. The computational cost associated with such a convolution is high due to the high complexity of evaluating $W_0$ at multiple frequencies. In this paper, we discuss how the cost of $G_0W_0$ calculation can be reduced by constructing a low rank approximation to the frequency dependent part of $W_0$. In particular, we examine the effect of such a low rank approximation on the accuracy of the $G_0W_0$ approximation. We also discuss how the numerical convolution of $G_0$ and $W_0$ can be evaluated efficiently and accurately by using a contour deformation technique with an appropriate choice of the contour.

  5. Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

    PubMed

    Zhou, Pan; Lin, Zhouchen; Zhang, Chao

    2016-05-01

    Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method. PMID:26080387

  6. Low-rank coal thermal properties and diffusivity: Final report

    SciTech Connect

    Ramirez, W.F.

    1987-06-01

    This project developed techniques for measuring thermal properties and mass diffusivities of low-rank coals and coal powders. Using the concept of volume averaging, predictive models have been developed for these porous media properties. The Hot Wire Method was used for simultaneously measuring the thermal conductivity and thermal diffusivity of both consolidated and unconsolidated low-rank coals. A new computer-interfaced experiment is presented and sample container designs developed for both coal powders and consolidated coals. A new mathematical model, based upon volume averaging, is presented for the prediction of these porous media properties. Velocity and temperature effects on liquid-phase dispersion through unconsolidated coal were determined. Radioactive tracer data were used to determine mass diffusivities. A new predictive mathematical model is presented based upon volume averaging. Vapor-phase diffusivity measurements of organic solvents in consolidated lignite coal are reported. An unsteady-state pressure response experiment with microcomputed-based data acquisition was developed to estimate dispersion coefficients through consolidated lignite coals. The mathematical analysis of the pressure response data provides the dispersion coefficient and the adsorption coefficient. 48 refs., 59 figs., 17 tabs.

  7. DISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELS

    PubMed Central

    SCHULER, ALEJANDRO; LIU, VINCENT; WAN, JOE; CALLAHAN, ALISON; UDELL, MADELEINE; STARK, DAVID E.; SHAH, NIGAM H.

    2016-01-01

    The practice of medicine is predicated on discovering commonalities or distinguishing characteristics among patients to inform corresponding treatment. Given a patient grouping (hereafter referred to as a phenotype), clinicians can implement a treatment pathway accounting for the underlying cause of disease in that phenotype. Traditionally, phenotypes have been discovered by intuition, experience in practice, and advancements in basic science, but these approaches are often heuristic, labor intensive, and can take decades to produce actionable knowledge. Although our understanding of disease has progressed substantially in the past century, there are still important domains in which our phenotypes are murky, such as in behavioral health or in hospital settings. To accelerate phenotype discovery, researchers have used machine learning to find patterns in electronic health records, but have often been thwarted by missing data, sparsity, and data heterogeneity. In this study, we use a flexible framework called Generalized Low Rank Modeling (GLRM) to overcome these barriers and discover phenotypes in two sources of patient data. First, we analyze data from the 2010 Healthcare Cost and Utilization Project National Inpatient Sample (NIS), which contains upwards of 8 million hospitalization records consisting of administrative codes and demographic information. Second, we analyze a small (N=1746), local dataset documenting the clinical progression of autism spectrum disorder patients using granular features from the electronic health record, including text from physician notes. We demonstrate that low rank modeling successfully captures known and putative phenotypes in these vastly different datasets. PMID:26776181

  8. Laplacian Regularized Low-Rank Representation and Its Applications.

    PubMed

    Yin, Ming; Gao, Junbin; Lin, Zhouchen

    2016-03-01

    Low-rank representation (LRR) has recently attracted a great deal of attention due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a given set of observed data corrupted with sparse errors, LRR aims at learning a lowest-rank representation of all data jointly. LRR has broad applications in pattern recognition, computer vision and signal processing. In the real world, data often reside on low-dimensional manifolds embedded in a high-dimensional ambient space. However, the LRR method does not take into account the non-linear geometric structures within data, thus the locality and similarity information among data may be missing in the learning process. To improve LRR in this regard, we propose a general Laplacian regularized low-rank representation framework for data representation where a hypergraph Laplacian regularizer can be readily introduced into, i.e., a Non-negative Sparse Hyper-Laplacian regularized LRR model (NSHLRR). By taking advantage of the graph regularizer, our proposed method not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information in data. The extensive experimental results on image clustering, semi-supervised image classification and dimensionality reduction tasks demonstrate the effectiveness of the proposed method. PMID:27046494

  9. Low-rank coal research: Volume 3, Combustion research: Final report. [Great Plains

    SciTech Connect

    Mann, M. D.; Hajicek, D. R.; Zobeck, B. J.; Kalmanovitch, D. P.; Potas, T. A.; Maas, D. J.; Malterer, T. J.; DeWall, R. A.; Miller, B. G.; Johnson, M. D.

    1987-04-01

    Volume III, Combustion Research, contains articles on fluidized bed combustion, advanced processes for low-rank coal slurry production, low-rank coal slurry combustion, heat engine utilization of low-rank coals, and Great Plains Gasification Plant. These articles have been entered individually into EDB and ERA. (LTN)

  10. CO2 Sequestration Potential of Texas Low-Rank Coals

    SciTech Connect

    Duane McVay; Walter Ayers, Jr.; Jerry Jensen; Jorge Garduno; Gonzola Hernandez; Rasheed Bello; Rahila Ramazanova

    2006-08-31

    Injection of CO{sub 2} in coalbeds is a plausible method of reducing atmospheric emissions of CO{sub 2}, and it can have the additional benefit of enhancing methane recovery from coal. Most previous studies have evaluated the merits of CO{sub 2} disposal in high-rank coals. The objective of this research was to determine the technical and economic feasibility of CO{sub 2} sequestration in, and enhanced coalbed methane (ECBM) recovery from, low-rank coals in the Texas Gulf Coast area. Our research included an extensive coal characterization program, including acquisition and analysis of coal core samples and well transient test data. We conducted deterministic and probabilistic reservoir simulation and economic studies to evaluate the effects of injectant fluid composition (pure CO{sub 2} and flue gas), well spacing, injection rate, and dewatering on CO{sub 2} sequestration and ECBM recovery in low-rank coals of the Calvert Bluff formation of the Texas Wilcox Group. Shallow and deep Calvert Bluff coals occur in two, distinct, coalbed gas petroleum systems that are separated by a transition zone. Calvert Bluff coals < 3,500 ft deep are part of a biogenic coalbed gas system. They have low gas content and are part of a freshwater aquifer. In contrast, Wilcox coals deeper than 3,500 ft are part of a thermogenic coalbed gas system. They have high gas content and are part of a saline aquifer. CO{sub 2} sequestration and ECBM projects in Calvert Bluff low-rank coals of East-Central Texas must be located in the deeper, unmineable coals, because shallow Wilcox coals are part of a protected freshwater aquifer. Probabilistic simulation of 100% CO{sub 2} injection into 20 feet of Calvert Bluff coal in an 80-acre 5-spot pattern indicates that these coals can store 1.27 to 2.25 Bcf of CO{sub 2} at depths of 6,200 ft, with an ECBM recovery of 0.48 to 0.85 Bcf. Simulation results of flue gas injection (87% N{sub 2}-13% CO{sub 2}) indicate that these same coals can store 0.34 to 0

  11. DEVELOPMENT OF CARBON PRODUCTS FROM LOW-RANK COALS

    SciTech Connect

    Edwin S. Olson

    2001-07-01

    The goal of this project is to facilitate the production of carbon fibers from low-rank coal (LRC) tars. To this end, the effect of demineralization on the tar yields and composition was investigated using high-sodium and high-calcium lignites commonly mined in North Dakota. These coals were demineralized by ion exchange with ammonium acetate and by cation dissolution with nitric acid. Two types of thermal processing were investigated for obtaining suitable precursors for pitch and fiber production. Initially, tars were produced by simple pyrolysis of the set of samples at 650 C. Since these experiments produced little usable material from any of the samples, the coals were heated at moderate temperatures (380 and 400 C) in tetralin solvent to form and extract the plastic material (metaplast) that forms at these temperatures.

  12. Denoised Wigner distribution deconvolution via low-rank matrix completion.

    PubMed

    Lee, Justin; Barbastathis, George

    2016-09-01

    Wigner distribution deconvolution (WDD) is a decades-old method for recovering phase from intensity measurements. Although the technique offers an elegant linear solution to the quadratic phase retrieval problem, it has seen limited adoption due to its high computational/memory requirements and the fact that the technique often exhibits high noise sensitivity. Here, we propose a method for noise suppression in WDD via low-rank noisy matrix completion. Our technique exploits the redundancy of an object's phase space to denoise its WDD reconstruction. We show in model calculations that our technique outperforms other WDD algorithms as well as modern iterative methods for phase retrieval such as ptychography. Our results suggest that a class of phase retrieval techniques relying on regularized direct inversion of ptychographic datasets (instead of iterative reconstruction techniques) can provide accurate quantitative phase information in the presence of high levels of noise. PMID:27607616

  13. CO2 SEQUESTRATION POTENTIAL OF TEXAS LOW-RANK COALS

    SciTech Connect

    Duane A. Mcvay; Walter B. Ayers, Jr.; Jerry L. Jensen

    2004-02-01

    The objectives of this project are to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (CBM) recovery as an added benefit of sequestration. The primary objectives for this reporting period were to construct a coal geological model for reservoir analysis and to continue modeling studies of CO{sub 2} sequestration performance in coalbed methane reservoirs under various operational conditions. Detailed correlation of coal zones is important for reservoir analysis and modeling. Therefore, we interpreted and created isopleth maps of coal occurrences, and correlated individual coal seams within the coal bearing subdivisions of the Wilcox Group--the Hooper, Simsboro and Calvert Bluff formations. Preliminary modeling studies were run to determine if gravity effects would affect the performance of CO{sub 2} sequestration in coalbed methane reservoirs. Results indicated that gravity could adversely affect sweep efficiency and, thus, volumes of CO{sub 2} sequestered and methane produced in thick, vertically continuous coals. Preliminary modeling studies were also run to determine the effect of injection gas composition on sequestration in low-rank coalbeds. Injected gas composition was varied from pure CO{sub 2} to pure N{sub 2}, and results show that increasing N{sub 2} content degrades CO{sub 2} sequestration and methane production performance. We have reached a Data Exchange Agreement with Anadarko Petroleum Corporation. We are currently incorporating the Anadarko data into our work, and expect these data to greatly enhance the accuracy and value of our studies.

  14. An integrated approach to the utilization of low rank coals and biofuel

    SciTech Connect

    Sen, S.; Sen, M.; Moitra, N.

    1999-08-01

    While suggesting an integrated approach for utilization of inferior low rank coals for power in India, the importance of low temperature carbonization followed by retrieval of all value-based products has been stressed. It is further suggested that tar, obtained in the process, could be hydrogenated and fractionated in a central plant for conversion to hydrocarbons. High ash char, the principal product of pyrolysis, has been experimentally found to be amenable to beneficiation, yielding suitable fractions for power generation, briquetting, or blending. Experimental studies have shown that forest litters and agricultural wastes, containing significant proportions of spore, cuticle, and exine--considered as precursors of hydrocarbon-generating coal macerals--also yield large quantities of tar, ammonical liquor, and the principal product, char, which can be respectively utilized for the production of petroleum substitutes, value-based chemicals, and source material for blending, briquette making, and char-water slurries, opening up new avenues for fuel utilization and conservation.

  15. Statistically efficient tomography of low rank states with incomplete measurements

    NASA Astrophysics Data System (ADS)

    Acharya, Anirudh; Kypraios, Theodore; Guţă, Mădălin

    2016-04-01

    The construction of physically relevant low dimensional state models, and the design of appropriate measurements are key issues in tackling quantum state tomography for large dimensional systems. We consider the statistical problem of estimating low rank states in the set-up of multiple ions tomography, and investigate how the estimation error behaves with a reduction in the number of measurement settings, compared with the standard ion tomography setup. We present extensive simulation results showing that the error is robust with respect to the choice of states of a given rank, the random selection of settings, and that the number of settings can be significantly reduced with only a negligible increase in error. We present an argument to explain these findings based on a concentration inequality for the Fisher information matrix. In the more general setup of random basis measurements we use this argument to show that for certain rank r states it suffices to measure in O(r{log}d) bases to achieve the average Fisher information over all bases. We present numerical evidence for random states of up to eight atoms, which suggests that a similar behaviour holds in the case of Pauli bases measurements, for randomly chosen states. The relation to similar problems in compressed sensing is also discussed.

  16. Missing Modality Transfer Learning via Latent Low-Rank Constraint.

    PubMed

    Ding, Zhengming; Shao, Ming; Fu, Yun

    2015-11-01

    Transfer learning is usually exploited to leverage previously well-learned source domain for evaluating the unknown target domain; however, it may fail if no target data are available in the training stage. This problem arises when the data are multi-modal. For example, the target domain is in one modality, while the source domain is in another. To overcome this, we first borrow an auxiliary database with complete modalities, then consider knowledge transfer across databases and across modalities within databases simultaneously in a unified framework. The contributions are threefold: 1) a latent factor is introduced to uncover the underlying structure of the missing modality from the known data; 2) transfer learning in two directions allows the data alignment between both modalities and databases, giving rise to a very promising recovery; and 3) an efficient solution with theoretical guarantees to the proposed latent low-rank transfer learning algorithm. Comprehensive experiments on multi-modal knowledge transfer with missing target modality verify that our method can successfully inherit knowledge from both auxiliary database and source modality, and therefore significantly improve the recognition performance even when test modality is inaccessible in the training stage. PMID:26241972

  17. Proceedings of the 2nd symposium on molten carbonate fuel cell technology

    SciTech Connect

    Selman, J.R. ); Maru, H.C. ); Shores, D.A. ); Uchida, I. )

    1990-01-01

    This book contains papers presented at the International Symposium on Carbonate Fuel Cells held at the 178th meeting of the Electrochemical Society in Seattle, WA, October 1990. The development of the MCFC has been rapidly accelerating during the last decade, and MCFC commercialization has become an international goal. As the emphasis of development has been shifting from single-cell testing to stack design and long-term performance, the role of basic research also has broadened. This volume provides an overview of recent advances in the fundamental knowledge base supporting MCFC development and is intended to help define the future directions of research. As the commercialization of the MCFC becomes a reality, issues of manufacturing technology as well as the need to further improve long-term performance will dictate those directions.

  18. CO2 SEQUESTRATION POTENTIAL OF TEXAS LOW-RANK COALS

    SciTech Connect

    Duane A. McVay; Walter B. Ayers Jr.; Jerry L. Jensen

    2003-10-01

    The objectives of this project are to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (CBM) recovery as an added benefit of sequestration. The main objective for this reporting period was to further characterize the three areas selected as potential CO{sub 2} sequestration sites. Well-log data are critical for defining depth, thickness, number, and grouping of coal seams at the proposed sequestration sites. Thus, we purchased 12 hardcopy well logs (in addition to 15 well logs obtained during previous quarter) from a commercial source and digitized them to make coal-occurrence maps and cross sections. Detailed correlation of coal zones is important for reservoir analysis and modeling. Thus, we correlated and mapped Wilcox Group subdivisions--the Hooper, Simsboro and Calvert Bluff formations, as well as the coal-bearing intervals of the Yegua and Jackson formations in well logs. To assess cleat properties and describe coal characteristics, we made field trips to Big Brown and Martin Lake coal mines. This quarter we also received CO{sub 2} and methane sorption analyses of the Sandow Mine samples, and we are assessing the results. GEM, a compositional simulator developed by the Computer Modeling Group (CMG), was selected for performing the CO{sub 2} sequestration and enhanced CBM modeling tasks for this project. This software was used to conduct preliminary CO{sub 2} sequestration and methane production simulations in a 5-spot injection pattern. We are continuing to pursue a cooperative agreement with Anadarko Petroleum, which has already acquired significant relevant data near one of our potential sequestration sites.

  19. Spectral thresholding quantum tomography for low rank states

    NASA Astrophysics Data System (ADS)

    Butucea, Cristina; Guţă, Mădălin; Kypraios, Theodore

    2015-11-01

    The estimation of high dimensional quantum states is an important statistical problem arising in current quantum technology applications. A key example is the tomography of multiple ions states, employed in the validation of state preparation in ion trap experiments (Häffner et al 2005 Nature 438 643). Since full tomography becomes unfeasible even for a small number of ions, there is a need to investigate lower dimensional statistical models which capture prior information about the state, and to devise estimation methods tailored to such models. In this paper we propose several new methods aimed at the efficient estimation of low rank states and analyse their performance for multiple ions tomography. All methods consist in first computing the least squares estimator, followed by its truncation to an appropriately chosen smaller rank. The latter is done by setting eigenvalues below a certain ‘noise level’ to zero, while keeping the rest unchanged, or normalizing them appropriately. We show that (up to logarithmic factors in the space dimension) the mean square error of the resulting estimators scales as r\\cdot d/N where r is the rank, d={2}k is the dimension of the Hilbert space, and N is the number of quantum samples. Furthermore we establish a lower bound for the asymptotic minimax risk which shows that the above scaling is optimal. The performance of the estimators is analysed in an extensive simulations study, with emphasis on the dependence on the state rank, and the number of measurement repetitions. We find that all estimators perform significantly better than the least squares, with the ‘physical estimator’ (which is a bona fide density matrix) slightly outperforming the other estimators.

  20. CO2 Sequestration Potential of Texas Low-Rank Coals

    SciTech Connect

    Duane A. McVay; Walter B. Ayers Jr; Jerry L. Jensen

    2003-07-01

    The objective of this project is to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (CBM) recovery as an added benefit of sequestration. The main objectives for this reporting period were to further characterize the three areas selected as potential test sites, to begin assessing regional attributes of natural coal fractures (cleats), which control coalbed permeability, and to interview laboratories for coal sample testing. An additional objective was to initiate discussions with an operating company that has interests in Texas coalbed gas production and CO{sub 2} sequestration potential, to determine their interest in participation and cost sharing in this project. Well-log data are critical for defining depth, thickness, number, and grouping of coal seams at the proposed sequestration sites. Therefore, we purchased 15 well logs from a commercial source to make coal-occurrence maps and cross sections. Log suites included gamma ray (GR), self potential (SP), resistivity, sonic, and density curves. Other properties of the coals in the selected areas were collected from published literature. To assess cleat properties and describe coal characteristics, we made field trips to a Jackson coal outcrop and visited Wilcox coal exposures at the Sandow surface mine. Coal samples at the Sandow mine were collected for CO{sub 2} and methane sorption analyses. We contacted several laboratories that specialize in analyzing coals and selected a laboratory, submitting the Sandow Wilcox coals for analysis. To address the issue of cost sharing, we had fruitful initial discussions with a petroleum corporation in Houston. We reviewed the objectives and status of this project, discussed data that they have already collected, and explored the potential for cooperative data acquisition and exchange in the future. We are pursuing a cooperative agreement with them.

  1. Ultrafine grinding of low-rank coal: Final report

    SciTech Connect

    Bouchillon, C.W.; Steele, W.G.

    1986-08-01

    A study of ultrafine grinding of low-rank coals in a fluid-energy mill was undertaken. This report presents the results of the Phase I effort which included a review of the literature on ultrafine grinding, a review of theories of grinding, a combined grinding and drying experiment on Martin Lake Texas lignite, an evaluation of the energy requirements for the process, and an evaluation of the properties of the products from the grinding tests. A sample of Martin Lake Texas lignite was obtained and a series of tests were conducted in a fluid-energy mill at the Ergon, Inc., Micro-Energy Division development facility at Vicksburg, MS. The grinding fluids used were air at 116 F and steam at 225, 310, 350, 400, and 488 F as measured in the mill. The products of these tests were analyzed for volatile mattr, ash, total moisture, equilibrium moisture, heating value, density distribution, aerodynamic particle size classification, angle of repose, porosity, density, and particle size distribution. ASTM test procedures were followed where applicable. Ultimate and ash mineral analyses were also conducted on the samples. Results of the various tests are presented in detail in the report. In general, the fluid energy mill was used succssfully in simultaneous grinding and drying of the lignite. Particle size reduction to less than 10 microns on a population basis was achieved. The equilibrium moisture of the samples decreased with increasing grinding fluid temperatures. Density distribution studies showed that a significant fraction of the ash appeared in the >1.6 specific gravity particles. The energy required for the grinding/drying process increased with increasing mill temperatures. 29 refs., 18 figs., 13 tabs.

  2. Investigation of oxygen functional groups in low rank coal

    SciTech Connect

    Hagaman, E.W.; Lee, S.K.

    1993-07-01

    The distribution of the organic oxygen content of coals among the principal oxygen containing functional groups typically is determined by a combination of chemical and spectroscopic methods (1,2) and results in a classification scheme such as % carboxyl, % hydroxyl, % carbonyl, and % ether. A notable subdivision in this classification scheme is the differentiation of phenols in a coal on the basis of their ortho-substitution pattern (3). Apart from this distinction, the further classification of oxygen into functional group subsets is virtually nonexistent. This paper presents initial experiments that indicate a fuller characterization of oxygen distribution in low rank coal is possible. The experimental approach couples selective chemical perturbation and solid state NMR analysis of the material, specifically, the fluorination of Argonne Premium Coal {number_sign}8, North Dakota lignite, and spectroscopic examination by high resolution solid state {sup 19}F NMR (4). The fluorination reagent is diethylaminosulfur trifluoride (DAST), (Et){sub 2}NSF{sub 3}, which promotes a rich slate of oxygen functional group interconversions that introduce fluorine into the coal matrix (5). The virtual absence of this element in coals make {sup 19}F an attractive NMR nuclei for this application (6). The present experiments use direct detection of the {sup 19}F nucleus under conditions of proton ({sup 1}H) heteronuclear dipolar decoupling and magic angle spinning (MAS). The ca 300 ppm range of {sup 19}F chemical shifts in common carbon-fluorine bonding configurations and high {sup 19}F nuclear sensitivity permit the identification of unique and chemically dilute functional groups in the coal milieu. The unique detection of aromatic and aliphatic carboxylic acids and primary and secondary alcohols provide examples of the exquisite functional group detail that is revealed by this combination of techniques.

  3. CO2 SEQUESTRATION POTENTIAL OF TEXAS LOW-RANK COALS

    SciTech Connect

    Duane A. McVay; Walter B. Ayers, Jr.; Jerry L. Jensen

    2004-07-01

    The objectives of this project are to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (CBM) recovery as an added benefit of sequestration. The main tasks for this reporting period were to correlate well logs and refine coal property maps, evaluate methane content and gas composition of Wilcox Group coals, and initiate discussions concerning collection of additional, essential data with Anadarko. To assess the volume of CO{sub 2} that may be sequestered and volume of methane that can be produced in the vicinity of the proposed Sam Seymour sequestration site, we used approximately 200 additional wells logs from Anadarko Petroleum Corp. to correlate and map coal properties of the 3 coal-bearing intervals of Wilcox group. Among the maps we are making are maps of the number of coal beds, number of coal beds greater than 5 ft thick, and cumulative coal thickness for each coal interval. This stratigraphic analysis validates the presence of abundant coal for CO{sub 2} sequestration in the Wilcox Group in the vicinity of Sam Seymour power plant. A typical wellbore in this region may penetrate 20 to 40 coal beds with cumulative coal thickness between 80 and 110 ft. Gas desorption analyses of approximately 75 coal samples from the 3 Wilcox coal intervals indicate that average methane content of Wilcox coals in this area ranges between 216 and 276 scf/t, basinward of the freshwater boundary indicated on a regional hydrologic map. Vitrinite reflectance data indicate that Wilcox coals are thermally immature for gas generation in this area. Minor amounts of biogenic gas may be present, basinward of the freshwater line, but we infer that most of the Wilcox coalbed gas in the deep coal beds is migrated thermogenic gas. Analysis based on limited data suggest that sites for CO{sub 2} sequestration and enhanced coalbed gas recovery should be located basinward of the Wilcox

  4. Low-rank coal research: Volume 1, Control technology, liquefaction, and gasification: Final report

    SciTech Connect

    Weber, G.F.; Collings, M.E.; Schelkoph, G.L.; Steadman, E.N.; Moretti, C.J.; Henke, K.R.; Rindt, J.R.; Hetland, M.D.; Knudson, C.L.; Willson, W.G.

    1987-04-01

    Volume I contains articles on SO/sub x//NO/sub x/ control, waste management, low-rank direct liquefaction, hydrogen production from low-rank coals, and advanced wastewater treatment. These articles have been entered individually into EDB and ERA. (LTN)

  5. Ethyl-tertiary-butyl-ether (ETBE) as an aviation fuel: Eleventh international symposium on alcohol fuels

    SciTech Connect

    Maben, G.D.; Shauck, M.E.; Zanin, M.G.

    1996-12-31

    This paper discusses the preliminary flight testing of an aircraft using neat burning ethyl-tertiary-butyl-ether (ETBE) as a fuel. No additional changes were made to the fuel delivery systems which had previously been modified to provide the higher fuel flow rates required to operate the engine on neat ethanol. Air-fuel ratios were manually adjusted with the mixture control. This system allows the pilot to adjust the mixture to compensate for changes in air density caused by altitude, pressure and temperature. The engine was instrumented to measure exhaust gas temperatures (EGT), cylinder head temperatures (CHT), and fuel flows, while the standard aircraft instruments were used to collect aircraft performance data. Baseline engine data for ETBE and Avgas are compared. Preliminary data indicates the technical and economic feasibility of using ETBE as an aviation fuel for the piston engine fleet. Furthermore, the energy density of ETBE qualifies it as a candidate for a turbine engine fuel of which 16.2 billion gallons are used in the US each year.

  6. CO2 Sequestration Potential of Texas Low-Rank Coals

    SciTech Connect

    Duane A. McVay; Walter B. Ayers Jr; Jerry L. Jensen

    2005-10-01

    The objectives of this project are to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (ECBM) recovery as an added benefit of sequestration. The main objectives for this reporting period were to perform reservoir simulation and economic sensitivity studies to (1) determine the effects of injection gas composition, (2) determine the effects of injection rate, and (3) determine the effects of coal dewatering prior to CO{sub 2} injection on CO{sub 2} sequestration in the Lower Calvert Bluff Formation (LCB) of the Wilcox Group coals in east-central Texas. To predict CO{sub 2} sequestration and ECBM in LCB coal beds for these three sensitivity studies, we constructed a 5-spot pattern reservoir simulation model and selected reservoir parameters representative of a typical depth, approximately 6,200-ft, of potential LCB coalbed reservoirs in the focus area of East-Central Texas. Simulation results of flue gas injection (13% CO{sub 2} - 87% N{sub 2}) in an 80-acre 5-spot pattern (40-ac well spacing) indicate that LCB coals with average net thickness of 20 ft can store a median value of 0.46 Bcf of CO{sub 2} at depths of 6,200 ft, with a median ECBM recovery of 0.94 Bcf and median CO{sub 2} breakthrough time of 4,270 days (11.7 years). Simulation of 100% CO{sub 2} injection in an 80-acre 5-spot pattern indicated that these same coals with average net thickness of 20 ft can store a median value of 1.75 Bcf of CO{sub 2} at depths of 6,200 ft with a median ECBM recovery of 0.67 Bcf and median CO{sub 2} breakthrough time of 1,650 days (4.5 years). Breakthrough was defined as the point when CO{sub 2} comprised 5% of the production stream for all cases. The injection rate sensitivity study for pure CO{sub 2} injection in an 80-acre 5-spot pattern at 6,200-ft depth shows that total volumes of CO{sub 2} sequestered and methane produced do not have significant sensitivity to

  7. CO2 SEQUESTRATION POTENTIAL OF TEXAS LOW-RANK COALS

    SciTech Connect

    Duane A. McVay; Walter B. Ayers Jr; Jerry L. Jensen

    2004-11-01

    The objectives of this project are to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (CBM) recovery as an added benefit of sequestration. there were two main objectives for this reporting period. first, they wanted to collect wilcox coal samples from depths similar to those of probable sequestration sites, with the objective of determining accurate parameters for reservoir model description and for reservoir simulation. The second objective was to pursue opportunities for determining permeability of deep Wilcox coal to use as additional, necessary data for modeling reservoir performance during CO{sub 2} sequestration and enhanced coalbed methane recovery. In mid-summer, Anadarko Petroleum Corporation agreed to allow the authors to collect Wilcox Group coal samples from a well that was to be drilled to the Austin Chalk, which is several thousand feet below the Wilcox. In addition, they agreed to allow them to perform permeability tests in coal beds in an existing shut-in well. Both wells are in the region of the Sam K. Seymour power station, a site that they earlier identified as a major point source of CO{sub 2}. They negotiated contracts for sidewall core collection and core analyses, and they began discussions with a service company to perform permeability testing. To collect sidewall core samples of the Wilcox coals, they made structure and isopach maps and cross sections to select coal beds and to determine their depths for coring. On September 29, 10 sidewall core samples were obtained from 3 coal beds of the Lower Calvert Bluff Formation of the Wilcox Group. The samples were desorbed in 4 sidewall core canisters. Desorbed gas samples were sent to a laboratory for gas compositional analyses, and the coal samples were sent to another laboratory to measure CO{sub 2}, CH{sub 4}, and N{sub 2} sorption isotherms. All analyses should be finished by the end of

  8. Anaerobic biprocessing of low rank coals. Final technical report, September 12, 1990--August 10, 1993

    SciTech Connect

    Jain, M.K.; Narayan, R.

    1993-08-05

    Coal solubilization under aerobic conditions results in oxygenated coal product which, in turn, makes the coal poorer fuel than the starting material. A novel approach has been made in this project is to remove oxygen from coal by reductive decarboxylation. In Wyodak subbituminous coal the major oxygen functionality is carboxylic groups which exist predominantly as carboxylate anions strongly chelating metal cations like Ca{sup 2+} and forming strong macromolecular crosslinks which contribute in large measure to network polymer structure. Removal of the carboxylic groups at ambient temperature by anaerobic organisms would unravel the macromoleculer network, resulting in smaller coal macromolecules with increased H/C ratio which has better fuel value and better processing prospects. These studies described here sought to find biological methods to remove carboxylic functionalities from low rank coals under ambient conditions and to assess the properties of these modified coals towards coal liquefaction. Efforts were made to establish anaerobic microbial consortia having decarboxylating ability, decarboxylate coal with the adapted microbial consortia, isolate the organisms, and characterize the biotreated coal products. Production of CO{sup 2} was used as the primary indicator for possible coal decarboxylation.

  9. Low-rank-coal study national needs for resource development. Volume 1. Executive summary

    SciTech Connect

    Elliot, Dr., Martin A.; Hill, George R.; Jonakin, James; Crutchfield, Paul W.; Severson, Donald E.; White, David M.; Yeager, Kurt

    1980-11-01

    Low-rank coals - lignite and subbituminous - are those which have been subjected to the least amount of metamorphic change during the coal-forming process. As such, they retain greater fractions of moisture and volatile matter from the original peat material, and contain less fixed carbon, than the high-rank coals - bituminous and anthracite. The primary measure used to classify the lower ranks of coal is heating value. Other important characteristics which distinguish the low-rank coals from high-rank coals are discussed in this report. Low-rank coals represent a major, and largely untapped, energy resource for this country. Very extensive deposits of lignite and subbituminous coal exist in the western states, the Gulf coast, and Alaska. Major deposits of low-rank coal are also found in many other countries, most notably the USSR, Australia, Canada, and the central and eastern European nations. Worldwide coal statistics indicate that low-rank coals account for roughly one-third of the total resource and current production tonnages. This report recommends a comprehensive national research, development, and demonstration (RD and D) program to enhance the development of low-rank coals. The major conclusion of this study is that the unique properties of these coals affect the technologies for their extraction, preparation, direct use, and conversion and justify a separate focus on low-rank coals in the national RD and D efforts.

  10. Drying low rank coal and retarding spontaneous ignition

    SciTech Connect

    Bixel, J.C.; Bellow, E.J.; Heaney, W.F.; Facinelli, S.H.

    1989-05-09

    A method is described of producing a dried particulate coal fuel having a reduced tendency to ignite spontaneously comprising spraying and intimately mixing the dried coal with an aqueous emulsion of a material selected from the group consisting of foots oils, petrolatum filtrate, and hydrocracker recycle oil.

  11. An Overview of Low-Rank Matrix Recovery From Incomplete Observations

    NASA Astrophysics Data System (ADS)

    Davenport, Mark A.; Romberg, Justin

    2016-06-01

    Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. This paper provides an overview of modern techniques for exploiting low-rank structure to perform matrix recovery in these settings, providing a survey of recent advances in this rapidly-developing field. Specific attention is paid to the algorithms most commonly used in practice, the existing theoretical guarantees for these algorithms, and representative practical applications of these techniques.

  12. The Resource Potential of Low-rank Coalbed Methane in the Eastern Zone of Junggar Basin

    NASA Astrophysics Data System (ADS)

    Ou, Chenghua; Li, Chaochun; He, Jian

    The Eastern Zone of Junggar Basin is a typical and favorable low-rank coal CBM gathering area. This paper firstly, calculates and evaluates the CBM resources of 4 sections and 23 units divided in the Eastern Zone of Junggar Basin; then points out the favorable areas and their burial depth range for further exploration. The results here will provide practical guidance for the whole basin's low-rank CBM investigation and exploration, and impact the understanding of other low-rank CBM resources around the world.

  13. A case study of PFBC for low rank coals

    SciTech Connect

    Jansson, S.A.

    1995-12-01

    Pressurized Fluidized Combined-Cycle (PFBC) technology allows the efficient and environmentally friendly utilization of solid fuels for power and combined heat and power generation. With current PFBC technology, thermal efficiencies near 46%, on an LHV basis and with low condenser pressures, can be reached in condensing power plants. Further efficiency improvements to 50% or more are possible. PFBC plants are characterized by high thermal efficiency, compactness, and extremely good environmental performance. The PFBC plants which are now in operation in Sweden, the U.S. and Japan burn medium-ash, bituminous coal with sulfur contents ranging from 0.7 to 4%. A sub- bituminous {open_quotes}black lignite{close_quotes} with high levels of sulfur, ash and humidity, is used as fuel in a demonstration PFBC plant in Spain. Project discussions are underway, among others in Central and Eastern Europe, for the construction of PFBC plants which will burn lignite, oil-shale and also mixtures of coal and biomass with high efficiency and extremely low emissions. This paper will provide information about the performance data for PFBC plants when operating on a range of low grade coals and other solid fuels, and will summarize other advantages of this leading new clean coal technology.

  14. Soil attenuation of leachates from low-rank coal combustion wastes: a literature survey. [116 references

    SciTech Connect

    Gauntt, R. O.; DeOtte, R. E.; Slowey, J. F.; McFarland, A. R.

    1984-01-01

    In parallel with pursuing the goal of increased utilization of low-rank solid fuels, the US Department of Energy is investigating various aspects associated with the disposal of coal-combustion solid wastes. Concern has been expressed relative to the potential hazards presented by leachates from fly ash, bottom ash and scrubber wastes. This is of particular interest in some regions where disposal areas overlap aquifer recharge regions. The western regions of the United States are characterized by relatively dry alkaline soils which may effect substantial attenuation of contaminants in the leachates thereby reducing the pollution potential. A project has been initiated to study the contaminant uptake of western soils. This effort consists of two phases: (1) preparation of a state-of-the-art document on soil attenuation; and (2) laboratory experimental studies to characterize attenuation of a western soil. The state-of-the-art document, represented herein, presents the results of studies on the characteristics of selected wastes, reviews the suggested models which account for the uptake, discusses the specialized columnar laboratory studies on the interaction of leachates and soils, and gives an overview of characteristics of Texas and Wyoming soils. 116 references, 10 figures, 29 tables.

  15. The utilization of Indonesia`s low rank coal: Its potential, challenges and prospects

    SciTech Connect

    Panaka, P.

    1997-07-01

    It has known that there are around 36 billion tons of coal resources potential in Indonesia, however over 21 billion tons (58.7%) is classified as low-rank (lignite) coal. Due to their properties, these coals are not economical to be transported for a long distance and are therefore unexportable. That`s why these low-rank coals still under-utilized at present. As the utilization of low-rank coals is expected to grow in importance as the domestic`s demand for energy increases in the near future, efforts should also be directed to find the possible upgrading technology for low-rank coals by reducing the total moisture of it, once the possible upgrading technology has been adopted, then those coal can be converted into coal water mixture, coal liquefaction, gasification, briquetting, etc., even for mine mouth power-plant. The challenges facing low-rank coals are: low conversion efficiency resulting from the high moisture content and relatively low in calorific values, the risk of spontaneous combustion, ash deposit formation and higher CO{sub 2} emission To response to these challenges, the adoption of new and advanced technologies for the utilization of low-rank coals from the third countries is therefore required. Combined cycle technologies such as CFBC, PFBC and IGCC, etc. combined with coal up-grading technology are applicable to low-rank coals and are expected to become a major future power plant for Indonesia. The main question for low-rank coals is whether these plants can be competitive when the extra costs involved in up-grading (drying) the coal are taken into account.

  16. Thermolysis of phenethyl phenyl ether: A model of ether linkages in low rank coal

    SciTech Connect

    Britt, P.F.; Buchanan, A.C. III; Malcolm, E.A.

    1994-09-01

    Currently, an area of interest and frustration for coal chemists has been the direct liquefaction of low rank coal. Although low rank coals are more reactive than bituminous coals, they are more difficult to liquefy and offer lower liquefaction yields under conditions optimized for bituminous coals. Solomon, Serio, and co-workers have shown that: in the pyrolysis and liquefaction of low rank coals, a low temperature cross-linking reaction associated with oxygen functional groups occurs before tar evolution. A variety of pretreatments (demineralization, alkylation, and ion-exchange) have been shown to reduce these retrogressive reactions and increase tar yields, but the actual chemical reactions responsible for these processes have not been defined. In order to gain insight into the thermochemical reactions leading to cross-linking in low rank coal, we have undertaken a study of the pyrolysis of oxygen containing coal model compounds. Solid state NMR studies suggest that the alkyl aryl ether linkage may be present in modest amounts in low rank coal. Therefore, in this paper, we will investigate the thermolysis of phenethyl phenyl ether (PPE) as a model of 0-aryl ether linkages found in low rank coal, lignites, and lignin, an evolutionary precursor of coal. Our results have uncovered a new reaction channel that can account for 25% of the products formed. The impact of reaction conditions, including restricted mass transport, on this new reaction pathway and the role of oxygen functional groups in cross-linking reactions will be investigated.

  17. Low-rank coal study : national needs for resource development. Volume 2. Resource characterization

    SciTech Connect

    Not Available

    1980-11-01

    Comprehensive data are presented on the quantity, quality, and distribution of low-rank coal (subbituminous and lignite) deposits in the United States. The major lignite-bearing areas are the Fort Union Region and the Gulf Lignite Region, with the predominant strippable reserves being in the states of North Dakota, Montana, and Texas. The largest subbituminous coal deposits are in the Powder River Region of Montana and Wyoming, The San Juan Basin of New Mexico, and in Northern Alaska. For each of the low-rank coal-bearing regions, descriptions are provided of the geology; strippable reserves; active and planned mines; classification of identified resources by depth, seam thickness, sulfur content, and ash content; overburden characteristics; aquifers; and coal properties and characteristics. Low-rank coals are distinguished from bituminous coals by unique chemical and physical properties that affect their behavior in extraction, utilization, or conversion processes. The most characteristic properties of the organic fraction of low-rank coals are the high inherent moisture and oxygen contents, and the correspondingly low heating value. Mineral matter (ash) contents and compositions of all coals are highly variable; however, low-rank coals tend to have a higher proportion of the alkali components CaO, MgO, and Na/sub 2/O. About 90% of the reserve base of US low-rank coal has less than one percent sulfur. Water resources in the major low-rank coal-bearing regions tend to have highly seasonal availabilities. Some areas appear to have ample water resources to support major new coal projects; in other areas such as Texas, water supplies may be constraining factor on development.

  18. Annihilating Filter-Based Low-Rank Hankel Matrix Approach for Image Inpainting.

    PubMed

    Jin, Kyong Hwan; Ye, Jong Chul

    2015-11-01

    In this paper, we propose a patch-based image inpainting method using a low-rank Hankel structured matrix completion approach. The proposed method exploits the annihilation property between a shift-invariant filter and image data observed in many existing inpainting algorithms. In particular, by exploiting the commutative property of the convolution, the annihilation property results in a low-rank block Hankel structure data matrix, and the image inpainting problem becomes a low-rank structured matrix completion problem. The block Hankel structured matrices are obtained patch-by-patch to adapt to the local changes in the image statistics. To solve the structured low-rank matrix completion problem, we employ an alternating direction method of multipliers with factorization matrix initialization using the low-rank matrix fitting algorithm. As a side product of the matrix factorization, locally adaptive dictionaries can be also easily constructed. Despite the simplicity of the algorithm, the experimental results using irregularly subsampled images as well as various images with globally missing patterns showed that the proposed method outperforms existing state-of-the-art image inpainting methods. PMID:26087492

  19. Robust visual tracking via L 0 regularized local low-rank feature learning

    NASA Astrophysics Data System (ADS)

    Liu, Risheng; Bai, Shanshan; Su, Zhixun; Zhang, Changcheng; Sun, Chunhai

    2015-05-01

    Visual tracking is a fundamental task and has many applications in computer vision. We incorporate local dictionary and L0 regularized low-rank features into the particle filter framework to address this problem. Specifically, by developing an efficient L0 regularized sparse coding model to incrementally learn low-rank features for the tracking target and incorporating a local dictionary into low-rank features to build the observation model, we establish a robust online object tracking system. As a nontrivial byproduct, we also develop numerical algorithms to efficiently solve the resulting nonconvex optimization problems. Compared with conventional methods, which often directly use corrupted observations to form the dictionary, our low-rank feature-based dictionary successfully removes occlusions and exactly represents the intrinsic structure of the object. Furthermore, in contrast to the traditional holistic methods, the local strategy contains abundant partial and spatial information, thus enhancing the discrimination of our observation model. More importantly, the L0 norm-based hard sparse coding can successfully reduce the redundant information while preserving the intrinsic low-rank features of the target object, leading to a better appearance subspace updating scheme. Experimental results on challenging sequences show that our method consistently outperforms several state-of-the-art methods.

  20. Joint Low-Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification.

    PubMed

    Zhang, Zhao; Li, Fanzhang; Zhao, Mingbo; Zhang, Li; Yan, Shuicheng

    2016-06-01

    Recovering low-rank and sparse subspaces jointly for enhanced robust representation and classification is discussed. Technically, we first propose a transductive low-rank and sparse principal feature coding (LSPFC) formulation that decomposes given data into a component part that encodes low-rank sparse principal features and a noise-fitting error part. To well handle the outside data, we then present an inductive LSPFC (I-LSPFC). I-LSPFC incorporates embedded low-rank and sparse principal features by a projection into one problem for direct minimization, so that the projection can effectively map both inside and outside data into the underlying subspaces to learn more powerful and informative features for representation. To ensure that the learned features by I-LSPFC are optimal for classification, we further combine the classification error with the feature coding error to form a unified model, discriminative LSPFC (D-LSPFC), to boost performance. The model of D-LSPFC seamlessly integrates feature coding and discriminative classification, so the representation and classification powers can be enhanced. The proposed approaches are more general, and several recent existing low-rank or sparse coding algorithms can be embedded into our problems as special cases. Visual and numerical results demonstrate the effectiveness of our methods for representation and classification. PMID:27046875

  1. Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS) for Constrained MRI

    PubMed Central

    Haldar, Justin P.

    2014-01-01

    Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-rank matrices when the image has limited spatial support or slowly varying phase. Based on this, we develop a novel and flexible framework for constrained image reconstruction that uses low-rank matrix modeling of local k-space neighborhoods (LORAKS). A new regularization penalty and corresponding algorithm for promoting low-rank are also introduced. The potential of LORAKS is demonstrated with simulated and experimental data for a range of denoising and sparse-sampling applications. LORAKS is also compared against state-of-the-art methods like homodyne reconstruction, ℓ1-norm minimization, and total variation minimization, and is demonstrated to have distinct features and advantages. In addition, while calibration-based support and phase constraints are commonly used in existing methods, the LORAKS framework enables calibrationless use of these constraints. PMID:24595341

  2. PROCEEDINGS OF SYMPOSIUM ON ENVIRONMENTAL ASPECTS OF FUEL CONVERSION TECHNOLOGY - VI: A SYMPOSIUM ON COAL-BASED SYNFUELS HELD IN DENVER, COLORADO ON OCTOBER 26-30, 1981

    EPA Science Inventory

    The document summarizes or contains an abstract of each presentation made at the EPA-sponsored symposium, October 26-30, 1981, in Denver, CO. The symposium provided a forum for the exchange of ideas and for discussion of environmentally related information on coal gasification an...

  3. Low-rank coal study: national needs for resource development. Volume 3. Technology evaluation

    SciTech Connect

    Not Available

    1980-11-01

    Technologies applicable to the development and use of low-rank coals are analyzed in order to identify specific needs for research, development, and demonstration (RD and D). Major sections of the report address the following technologies: extraction; transportation; preparation, handling and storage; conventional combustion and environmental control technology; gasification; liquefaction; and pyrolysis. Each of these sections contains an introduction and summary of the key issues with regard to subbituminous coal and lignite; description of all relevant technology, both existing and under development; a description of related environmental control technology; an evaluation of the effects of low-rank coal properties on the technology; and summaries of current commercial status of the technology and/or current RD and D projects relevant to low-rank coals.

  4. Low-rank coal study. Volume 5. RD and D program evaluation

    SciTech Connect

    Not Available

    1980-11-01

    A national program is recommended for research, development, and demonstration (RD and D) of improved technologies for the enviromentally acceptable use of low-rank coals. RD and D project recommendations are outlined in all applicable technology areas, including extraction, transportation, preparation, handling and storage, conventional combustion and environmental control technology, fluidized bed combustion, gasification, liquefaction, and pyrolysis. Basic research topics are identified separately, as well as a series of crosscutting research activities addressing environmental, economic, and regulatory issues. The recommended RD and D activities are classified into Priority I and Priority II categories, reflecting their relative urgency and potential impact on the advancement of low-rank coal development. Summaries of ongoing research projects on low-rank coals in the US are presented in an Appendix, and the relationships of these ongoing efforts to the recommended RD and D program are discussed.

  5. Adaptive low-rank approximation and denoised Monte Carlo approach for high-dimensional Lindblad equations

    NASA Astrophysics Data System (ADS)

    Le Bris, C.; Rouchon, P.; Roussel, J.

    2015-12-01

    We present a twofold contribution to the numerical simulation of Lindblad equations. First, an adaptive numerical approach to approximate Lindblad equations using low-rank dynamics is described: a deterministic low-rank approximation of the density operator is computed, and its rank is adjusted dynamically, using an on-the-fly estimator of the error committed when reducing the dimension. On the other hand, when the intrinsic dimension of the Lindblad equation is too high to allow for such a deterministic approximation, we combine classical ensemble averages of quantum Monte Carlo trajectories and a denoising technique. Specifically, a variance reduction method based on the consideration of a low-rank dynamics as a control variate is developed. Numerical tests for quantum collapse and revivals show the efficiency of each approach, along with the complementarity of the two approaches.

  6. Low-Rank and Eigenface Based Sparse Representation for Face Recognition

    PubMed Central

    Hou, Yi-Fu; Sun, Zhan-Li; Chong, Yan-Wen; Zheng, Chun-Hou

    2014-01-01

    In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC). Firstly, the low-rank images of the face images of each individual in training subset are extracted by the Robust Principal Component Analysis (Robust PCA) to alleviate the influence of noises (e.g., illumination difference and occlusions). Secondly, Singular Value Decomposition (SVD) is applied to extract the eigenfaces from these low-rank and approximate images. Finally, we utilize these eigenfaces to construct a compact and discriminative dictionary for sparse representation. We evaluate our method on five popular databases. Experimental results demonstrate the effectiveness and robustness of our method. PMID:25334027

  7. A Non-Local Low-Rank Approach to Enforce Integrability.

    PubMed

    Badri, Hicham; Yahia, Hussein

    2016-08-01

    We propose a new approach to enforce integrability using recent advances in non-local methods. Our formulation consists in a sparse gradient data-fitting term to handle outliers together with a gradient-domain non-local low-rank prior. This regularization has two main advantages: 1) the low-rank prior ensures similarity between non-local gradient patches, which helps recovering high-quality clean patches from severe outliers corruption and 2) the low-rank prior efficiently reduces dense noise as it has been shown in recent image restoration works. We propose an efficient solver for the resulting optimization formulation using alternate minimization. Experiments show that the new method leads to an important improvement compared with previous optimization methods and is able to efficiently handle both outliers and dense noise mixed together. PMID:27214898

  8. Image restoration via patch orientation-based low-rank matrix approximation and nonlocal means

    NASA Astrophysics Data System (ADS)

    Zhang, Di; He, Jiazhong; Du, Minghui

    2016-03-01

    Low-rank matrix approximation and nonlocal means (NLM) are two popular techniques for image restoration. Although the basic principle for applying these two techniques is the same, i.e., similar image patches are abundant in the image, previously published related algorithms use either low-rank matrix approximation or NLM because they manipulate the information of similar patches in different ways. We propose a method for image restoration by jointly using low-rank matrix approximation and NLM in a unified minimization framework. To improve the accuracy of determining similar patches, we also propose a patch similarity measurement based on curvelet transform. Extensive experiments on image deblurring and compressive sensing image recovery validate that the proposed method achieves better results than many state-of-the-art algorithms in terms of both quantitative measures and visual perception.

  9. Upgrading low-rank coals by TEK-KOL`s Liquids From Coal technology

    SciTech Connect

    Wang, M.; Gibbens, R.J.; Weber, K.L.; Knotternerus, B.A.

    1997-12-31

    TEK-KOL is a partnership between SGI International of La Jolla, California, and a unit of Zeigler Coal Holding Company, Fairview Heights, Illinois. TEK-KOL`s Liquids From Coal (LFC) Process uses a mild gasification process to convert low-rank coals into value added products. Two primary products are generated as a result of LFC processing: (1) Process-Derived Fuel (PDF), a high heating value, clean burning solid fuel and carbon source for a variety of utility and industrial applications, and (2) Coal-Derived Liquid (CDL), a low sulfur hydrocarbon liquid suitable for fuel oil and chemical feedstock uses. Both PDF and CDL have been successfully utilized on a commercial scale. The LFC Process has been thoroughly demonstrated at the ENCOAL LFC Demonstration Plant at the Buckskin Mine in the Powder River Basin, Wyoming. The 1,000 short ton per day plant, constructed and operated at a cost of US $90 million, was designed and built to commercial standards. Construction and initial operating costs were partially funded by the US Department of Energy (DOE) under Round Three of the Clean Coal Technology Program. The plant employs commercially available equipment and state of the art control system, and best available control technologies insure compliance with strict environmental standards. It became operational in June 1992. In the last five years, the plant and its supporting facilities have operated in an integrated mode for more than 14,500 hours. The major pieces of equipment, including the large blowers, combustors, dryer, pyrolyzer, and cooler have operated far more hours overall considering hot standby and ramping operations. The equipment has been demonstrated to operate reliably. The plant has processed 246,900 short tons of raw coal and produced 114,900 short tons of PDF and 116,100 barrels of CDL. A multi-phase process to identify and develop technically and financially viable LFC projects has been developed by TEK-KOL. Commercialization of the LFC technology is

  10. [Study on Microwave Co-Pyrolysis of Low Rank Coal and Circulating Coal Gas].

    PubMed

    Zhou, Jun; Yang, Zhe; Liu, Xiao-feng; Wu, Lei; Tian, Yu-hong; Zhao, Xi-cheng

    2016-02-01

    The pyrolysis of low rank coal to produce bluecoke, coal tar and gas is considered to be the optimal method to realize its clean and efficient utilization. However, the current mainstream pyrolysis production technology generally has a certain particle size requirements for raw coal, resulting in lower yield and poorer quality of coal tar, lower content of effective components in coal gas such as H₂, CH₄, CO, etc. To further improve the yield of coal tar obtained from the pyrolysis of low rank coal and explore systematically the effect of microwave power, pyrolysis time and particle size of coal samples on the yield and composition of microwave pyrolysis products of low rank coal through the analysis and characterization of products with FTIR and GC-MS, introducing microwave pyrolysis of low rank coal into the microwave pyrolysis reactor circularly was suggested to carry out the co-pyrolysis experiment of the low rank coal and coal gas generated by the pyrolysis of low rank coal. The results indicated that the yield of the bluecoke and liquid products were up to 62.2% and 26.8% respectively when the optimal pyrolysis process conditions with the microwave power of 800W, pyrolysis time of 40 min, coal samples particle size of 5-10 mm and circulating coal gas flow rate of 0.4 L · min⁻¹ were selected. The infrared spectrogram of the bluecoke under different microwave power and pyrolysis time overlapped roughly. The content of functional groups with -OH, C==O, C==C and C−O from the bluecoke through the pyrolysis of particle size coal samples had a larger difference. To improve microwave power, prolonging pyrolysis time and reducing particle size of coal samples were conducive to converting heavy component to light one into coal tar. PMID:27209750

  11. Supervised descent method with low rank and sparsity constraints for robust face alignment

    NASA Astrophysics Data System (ADS)

    Sun, Yubao; Hu, Bin; Deng, Jiankang; Li, Xing

    2015-03-01

    Supervised Descent Method (SDM) learns the descent directions of nonlinear least square objective in a supervised manner, which has been efficiently used for face alignment. However, SDM still may fail in the cases of partial occlusions and serious pose variations. To deal with this issue, we present a new method for robust face alignment by utilizing the low rank prior of human face and enforcing sparse structure of the descent directions. Our approach consists of low rank face frontalization and sparse descent steps. Firstly, in terms of the low rank prior of face image, we recover such a low-rank face from its deformed image and the associated deformation despite significant distortion and corruption. Alignment of the recovered frontal face image is more simple and effective. Then, we propose a sparsity regularized supervised descent model by enforcing the sparse structure of the descent directions under the l1constraint, which makes the model more effective in computation and robust to partial occlusion. Extensive results on several benchmarks demonstrate that the proposed method is robust to facial occlusions and pose variations

  12. Low-rank coal research: Volume 2, Advanced research and technology development: Final report

    SciTech Connect

    Mann, M.D.; Swanson, M.L.; Benson, S.A.; Radonovich, L.; Steadman, E.N.; Sweeny, P.G.; McCollor, D.P.; Kleesattel, D.; Grow, D.; Falcone, S.K.

    1987-04-01

    Volume II contains articles on advanced combustion phenomena, combustion inorganic transformation; coal/char reactivity; liquefaction reactivity of low-rank coals, gasification ash and slag characterization, and fine particulate emissions. These articles have been entered individually into EDB and ERA. (LTN)

  13. Smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization.

    PubMed

    Lu, Canyi; Lin, Zhouchen; Yan, Shuicheng

    2015-02-01

    This paper presents a general framework for solving the low-rank and/or sparse matrix minimization problems, which may involve multiple nonsmooth terms. The iteratively reweighted least squares (IRLSs) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables and their weights. However, the traditional IRLS can only solve a sparse only or low rank only minimization problem with squared loss or an affine constraint. This paper generalizes IRLS to solve joint/mixed low-rank and sparse minimization problems, which are essential formulations for many tasks. As a concrete example, we solve the Schatten-p norm and l2,q-norm regularized low-rank representation problem by IRLS, and theoretically prove that the derived solution is a stationary point (globally optimal if p,q ≥ 1). Our convergence proof of IRLS is more general than previous one that depends on the special properties of the Schatten-p norm and l2,q-norm. Extensive experiments on both synthetic and real data sets demonstrate that our IRLS is much more efficient. PMID:25531948

  14. Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition

    NASA Astrophysics Data System (ADS)

    Cui, Xiaoguang; Tian, Yuan; Weng, Lubin; Yang, Yiping

    2014-01-01

    This paper presents a novel low-rank and sparse decomposition (LSD) based model for anomaly detection in hyperspectral images. In our model, a local image region is represented as a low-rank matrix plus spares noises in the spectral space, where the background can be explained by the low-rank matrix, and the anomalies are indicated by the sparse noises. The detection of anomalies in local image regions is formulated as a constrained LSD problem, which can be solved efficiently and robustly with a modified "Go Decomposition" (GoDec) method. To enhance the validity of this model, we adapts a "simple linear iterative clustering" (SLIC) superpixel algorithm to efficiently generate homogeneous local image regions i.e. superpixels in hyperspectral imagery, thus ensures that the background in local image regions satisfies the condition of low-rank. Experimental results on real hyperspectral data demonstrate that, compared with several known local detectors including RX detector, kernel RX detector, and SVDD detector, the proposed model can comfortably achieves better performance in satisfactory computation time.

  15. Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

    PubMed

    Peng, Yong; Lu, Bao-Liang; Wang, Suhang

    2015-05-01

    Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches. PMID:25634552

  16. Study of Indonesia low rank coal utilization on modified fixed bed gasification for combined cycle power plant

    NASA Astrophysics Data System (ADS)

    Hardianto, T.; Amalia, A. R.; Suwono, A.; Riauwindu, P.

    2015-09-01

    Gasification is a conversion process converting carbon-based solid fuel into gaseous products that have considerable amount of calorific value. One of the carbon-based solid fuel that serves as feed for gasification is coal. Gasification gaseous product is termed as syngas (synthetic gas) that is composed of several different gases. Syngas produced from gasification vary from one process to another, this is due to several factors which are: feed characteristics, operation condition, gasified fluid condition, and gasification method or technology. One of the utilization of syngas is for combined cycle power plant fuel. In order to meet the need to convert carbon-based solid fuel into gaseous fuel for combined cycle power plant, engineering adjustment for gasification was done using related software to create the syngas with characteristics of natural gas that serve as fuel for combined cycle power plant in Indonesia. Feed used for the gasification process in this paper was Indonesian Low Rank Coal and the method used to obtain syngas was Modified Fixed Bed Gasifier. From the engineering adjustment process, the yielded syngas possessed lower heating value as much as 31828.32 kJ/kg in gasification condition of 600°C, 3.5 bar, and steam to feed ratio was 1 kg/kg. Syngas characteristics obtained from the process was used as a reference for the adjustment of the fuel system modification in combined cycle power plant that will have the same capacity with the conversion of the system's fuel from natural gas to syngas.

  17. Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers

    PubMed Central

    Zhu, Hongtu; Khondker, Zakaria; Lu, Zhaohua; Ibrahim, Joseph G.

    2014-01-01

    We propose a Bayesian generalized low rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing is developed to identify significant covariates on high-dimensional responses. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of GLRR and its comparison with several competing approaches. We apply GLRR to investigate the impact of 1,071 SNPs on top 40 genes reported by AlzGene database on the volumes of 93 regions of interest (ROI) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI). PMID:25349462

  18. Dynamic networks community detection via low rank component recovery of adjacency matrices

    NASA Astrophysics Data System (ADS)

    Bao, Wei; Michailidis, George

    Dynamic community detection in networks has been of high interest due to its various applications. In this work, we apply low rank extraction technique on adjacency matrices to approximate the community structures. Not only can we accurately identify the phase transition time points where significant changes in the community structures occur, but also we can increase the accuracy of the core community structures recovered in the `peace' time ranges by averaging the low rank components. A systematic methodology has been proposed as how to accomplish the target. Factor model, and stochastic block model (including weighted scenario) have been tested for the robustness of our model. Besides, applications on both Kuramoto model and US Senate Roll Call data are also carried out and interesting results are obtained.

  19. Highly accelerated cardiac cine parallel MRI using low-rank matrix completion and partial separability model

    NASA Astrophysics Data System (ADS)

    Lyu, Jingyuan; Nakarmi, Ukash; Zhang, Chaoyi; Ying, Leslie

    2016-05-01

    This paper presents a new approach to highly accelerated dynamic parallel MRI using low rank matrix completion, partial separability (PS) model. In data acquisition, k-space data is moderately randomly undersampled at the center kspace navigator locations, but highly undersampled at the outer k-space for each temporal frame. In reconstruction, the navigator data is reconstructed from undersampled data using structured low-rank matrix completion. After all the unacquired navigator data is estimated, the partial separable model is used to obtain partial k-t data. Then the parallel imaging method is used to acquire the entire dynamic image series from highly undersampled data. The proposed method has shown to achieve high quality reconstructions with reduction factors up to 31, and temporal resolution of 29ms, when the conventional PS method fails.

  20. Target detection in GPR data using joint low-rank and sparsity constraints

    NASA Astrophysics Data System (ADS)

    Bouzerdoum, Abdesselam; Tivive, Fok Hing Chi; Abeynayake, Canicious

    2016-05-01

    In ground penetrating radars, background clutter, which comprises the signals backscattered from the rough, uneven ground surface and the background noise, impairs the visualization of buried objects and subsurface inspections. In this paper, a clutter mitigation method is proposed for target detection. The removal of background clutter is formulated as a constrained optimization problem to obtain a low-rank matrix and a sparse matrix. The low-rank matrix captures the ground surface reflections and the background noise, whereas the sparse matrix contains the target reflections. An optimization method based on split-Bregman algorithm is developed to estimate these two matrices from the input GPR data. Evaluated on real radar data, the proposed method achieves promising results in removing the background clutter and enhancing the target signature.

  1. On low-rank updates to the singular value and Tucker decompositions

    SciTech Connect

    O'Hara, M J

    2009-10-06

    The singular value decomposition is widely used in signal processing and data mining. Since the data often arrives in a stream, the problem of updating matrix decompositions under low-rank modification has been widely studied. Brand developed a technique in 2006 that has many advantages. However, the technique does not directly approximate the updated matrix, but rather its previous low-rank approximation added to the new update, which needs justification. Further, the technique is still too slow for large information processing problems. We show that the technique minimizes the change in error per update, so if the error is small initially it remains small. We show that an updating algorithm for large sparse matrices should be sub-linear in the matrix dimension in order to be practical for large problems, and demonstrate a simple modification to the original technique that meets the requirements.

  2. A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs

    PubMed Central

    Li, Feifei; Piao, Minghao; Piao, Yongjun; Li, Meijing; Ryu, Keun Ho

    2014-01-01

    Objectives Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification. Methods We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification. Results The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. Conclusion Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification. PMID:25389514

  3. Anaerobic bioprocessing of low-rank coals. [Veillonella alcalescens and Propionibacterium acidipropionici

    SciTech Connect

    Jain, M.K.; Narayan, R.; Han, O.

    1992-01-30

    The overall goal of this project is to find biological methods to remove carboxylic functionalities from low-rank coals under ambient conditions and to assess the properties of these modified coals towards coal liquefaction. The main objectives of this quarter were: (1) continuation of microbial consortia development, (2) evaluation of the isolated organisms for decarboxylation, (3) selection of best performing culture (known cultures vs. new isolates), and (4) coal decarboxylation using activated carbon as blanks. The project began on September 12, 1990.

  4. Anaerobic bioprocessing of low-rank coals. Quarterly progress report, October 1--December 31, 1991

    SciTech Connect

    Jain, M.K.; Narayan, R.; Han, O.

    1992-01-30

    The overall goal of this project is to find biological methods to remove carboxylic functionalities from low-rank coals under ambient conditions and to assess the properties of these modified coals towards coal liquefaction. The main objectives of this quarter were: (1) continuation of microbial consortia development, (2) evaluation of the isolated organisms for decarboxylation, (3) selection of best performing culture (known cultures vs. new isolates), and (4) coal decarboxylation using activated carbon as blanks. The project began on September 12, 1990.

  5. Low-rank coal research under the UND/DOE cooperative agreement. Quarterly technical progress report, April 1983-June 1983

    SciTech Connect

    Wiltsee, Jr., G. A.

    1983-01-01

    Progress reports are presented for the following tasks: (1) gasification wastewater treatment and reuse; (2) fine coal cleaning; (3) coal-water slurry preparation; (4) low-rank coal liquefaction; (5) combined flue gas cleanup/simultaneous SO/sub x/-NO/sub x/ control; (6) particulate control and hydrocarbons and trace element emissions from low-rank coals; (7) waste characterization; (8) combustion research and ash fowling; (9) fluidized-bed combustion of low-rank coals; (10) ash and slag characterization; (11) organic structure of coal; (12) distribution of inorganics in low-rank coals; (13) physical properties and moisture of low-rank coals; (14) supercritical solvent extraction; and (15) pyrolysis and devolatilization.

  6. Low-rank coal drying technologies current status and new developments

    SciTech Connect

    Karthikeyan, M.; Wu, Z.H.; Mujumdar, A.S.

    2009-07-01

    Despite their vast reserves, low-rank coals are considered undesirable because their high moisture content entails high transportation costs, potential safety hazards in transportation and storage, and the low thermal efficiency obtained in combustion of such coals. Their high moisture content, greater tendency to combust spontaneously, high degree of weathering, and the dusting characteristics restrict widespread use of such coals. The price of coal sold to utilities depends upon the heating value of the coal. Thus, removal of moisture from low-rank coals (LRC) is an important operation. Furthermore, LRC can be used cost effectively for pyrolysis, gasification, and liquefaction processes. This article provides an overview the diverse processes both those that utilize conventional drying technologies and those that are not yet commercialized and hence in need of RD. Relative merits and limitations of the various technologies and the current state of their development are presented. Drying characteristics of low-rank coal as well as factors affecting drying characteristics of coal samples are also discussed.

  7. Efficient completion for corrupted low-rank images via alternating direction method

    NASA Astrophysics Data System (ADS)

    Li, Wei; Zhao, Lei; Xu, Duanqing; Lu, Dongming

    2014-05-01

    We propose an efficient and easy-to-implement method to settle the inpainting problem for low-rank images following the recent studies about low-rank matrix completion. In general, our method has three steps: first, corresponding to the three channels of RGB color space, an incomplete image is split into three incomplete matrices; second, each matrix is restored by solving a convex problem derived from the nuclear norm relaxation; at last, the three recovered matrices are merged to produce the final output. During the process, in order to efficiently solve the nuclear norm minimization problem, we employ the alternating direction method. Except for the basic image inpainting problem, we also enable our method to handle cases where corrupted images not only have missing values but also have noisy entries. Our experiments show that our method outperforms the existing inpainting techniques both quantitatively and qualitatively. We also demonstrate that our method is capable of processing many other situations, including block-wise low-rank image completion, large-scale image restoration, and object removal.

  8. Color correction with blind image restoration based on multiple images using a low-rank model

    NASA Astrophysics Data System (ADS)

    Li, Dong; Xie, Xudong; Lam, Kin-Man

    2014-03-01

    We present a method that can handle the color correction of multiple photographs with blind image restoration simultaneously and automatically. We prove that the local colors of a set of images of the same scene exhibit the low-rank property locally both before and after a color-correction operation. This property allows us to correct all kinds of errors in an image under a low-rank matrix model without particular priors or assumptions. The possible errors may be caused by changes of viewpoint, large illumination variations, gross pixel corruptions, partial occlusions, etc. Furthermore, a new iterative soft-segmentation method is proposed for local color transfer using color influence maps. Due to the fact that the correct color information and the spatial information of images can be recovered using the low-rank model, more precise color correction and many other image-restoration tasks-including image denoising, image deblurring, and gray-scale image colorizing-can be performed simultaneously. Experiments have verified that our method can achieve consistent and promising results on uncontrolled real photographs acquired from the Internet and that it outperforms current state-of-the-art methods.

  9. Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation.

    PubMed

    Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung

    2016-08-01

    Low-rank representation (LRR) has been successfully applied in exploring the subspace structures of data. However, in previous LRR-based semi-supervised subspace clustering methods, the label information is not used to guide the affinity matrix construction so that the affinity matrix cannot deliver strong discriminant information. Moreover, these methods cannot guarantee an overall optimum since the affinity matrix construction and subspace clustering are often independent steps. In this paper, we propose a robust semi-supervised subspace clustering method based on non-negative LRR (NNLRR) to address these problems. By combining the LRR framework and the Gaussian fields and harmonic functions method in a single optimization problem, the supervision information is explicitly incorporated to guide the affinity matrix construction and the affinity matrix construction and subspace clustering are accomplished in one step to guarantee the overall optimum. The affinity matrix is obtained by seeking a non-negative low-rank matrix that represents each sample as a linear combination of others. We also explicitly impose the sparse constraint on the affinity matrix such that the affinity matrix obtained by NNLRR is non-negative low-rank and sparse. We introduce an efficient linearized alternating direction method with adaptive penalty to solve the corresponding optimization problem. Extensive experimental results demonstrate that NNLRR is effective in semi-supervised subspace clustering and robust to different types of noise than other state-of-the-art methods. PMID:26259210

  10. Simultaneously Sparse and Low-Rank Abundance Matrix Estimation for Hyperspectral Image Unmixing

    NASA Astrophysics Data System (ADS)

    Giampouras, Paris V.; Themelis, Konstantinos E.; Rontogiannis, Athanasios A.; Koutroumbas, Konstantinos D.

    2016-08-01

    In a plethora of applications dealing with inverse problems, e.g. in image processing, social networks, compressive sensing, biological data processing etc., the signal of interest is known to be structured in several ways at the same time. This premise has recently guided the research to the innovative and meaningful idea of imposing multiple constraints on the parameters involved in the problem under study. For instance, when dealing with problems whose parameters form sparse and low-rank matrices, the adoption of suitably combined constraints imposing sparsity and low-rankness, is expected to yield substantially enhanced estimation results. In this paper, we address the spectral unmixing problem in hyperspectral images. Specifically, two novel unmixing algorithms are introduced, in an attempt to exploit both spatial correlation and sparse representation of pixels lying in homogeneous regions of hyperspectral images. To this end, a novel convex mixed penalty term is first defined consisting of the sum of the weighted $\\ell_1$ and the weighted nuclear norm of the abundance matrix corresponding to a small area of the image determined by a sliding square window. This penalty term is then used to regularize a conventional quadratic cost function and impose simultaneously sparsity and row-rankness on the abundance matrix. The resulting regularized cost function is minimized by a) an incremental proximal sparse and low-rank unmixing algorithm and b) an algorithm based on the alternating minimization method of multipliers (ADMM). The effectiveness of the proposed algorithms is illustrated in experiments conducted both on simulated and real data.

  11. Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty.

    PubMed

    Kim, Kyungsang; Ye, Jong Chul; Worstell, William; Ouyang, Jinsong; Rakvongthai, Yothin; El Fakhri, Georges; Li, Quanzheng

    2015-03-01

    Spectral computed tomography (CT) is a promising technique with the potential for improving lesion detection, tissue characterization, and material decomposition. In this paper, we are interested in kVp switching-based spectral CT that alternates distinct kVp X-ray transmissions during gantry rotation. This system can acquire multiple X-ray energy transmissions without additional radiation dose. However, only sparse views are generated for each spectral measurement; and the spectra themselves are limited in number. To address these limitations, we propose a penalized maximum likelihood method using spectral patch-based low-rank penalty, which exploits the self-similarity of patches that are collected at the same position in spectral images. The main advantage is that the relatively small number of materials within each patch allows us to employ the low-rank penalty that is less sensitive to intensity changes while preserving edge directions. In our optimization formulation, the cost function consists of the Poisson log-likelihood for X-ray transmission and the nonconvex patch-based low-rank penalty. Since the original cost function is difficult to minimize directly, we propose an optimization method using separable quadratic surrogate and concave convex procedure algorithms for the log-likelihood and penalty terms, which results in an alternating minimization that provides a computational advantage because each subproblem can be solved independently. We performed computer simulations and a real experiment using a kVp switching-based spectral CT with sparse-view measurements, and compared the proposed method with conventional algorithms. We confirmed that the proposed method improves spectral images both qualitatively and quantitatively. Furthermore, our GPU implementation significantly reduces the computational cost. PMID:25532170

  12. Effect of Water Invasion on Outburst Predictive Index of Low Rank Coals in Dalong Mine.

    PubMed

    Jiang, Jingyu; Cheng, Yuanping; Mou, Junhui; Jin, Kan; Cui, Jie

    2015-01-01

    To improve the coal permeability and outburst prevention, coal seam water injection and a series of outburst prevention measures were tested in outburst coal mines. These methods have become important technologies used for coal and gas outburst prevention and control by increasing the external moisture of coal or decreasing the stress of coal seam and changing the coal pore structure and gas desorption speed. In addition, techniques have had a significant impact on the gas extraction and outburst prevention indicators of coal seams. Globally, low rank coals reservoirs account for nearly half of hidden coal reserves and the most obvious feature of low rank coal is the high natural moisture content. Moisture will restrain the gas desorption and will affect the gas extraction and accuracy of the outburst prediction of coals. To study the influence of injected water on methane desorption dynamic characteristics and the outburst predictive index of coal, coal samples were collected from the Dalong Mine. The methane adsorption/desorption test was conducted on coal samples under conditions of different injected water contents. Selective analysis assessed the variations of the gas desorption quantities and the outburst prediction index (coal cutting desorption index). Adsorption tests indicated that the Langmuir volume of the Dalong coal sample is ~40.26 m3/t, indicating a strong gas adsorption ability. With the increase of injected water content, the gas desorption amount of the coal samples decreased under the same pressure and temperature. Higher moisture content lowered the accumulation desorption quantity after 120 minutes. The gas desorption volumes and moisture content conformed to a logarithmic relationship. After moisture correction, we obtained the long-flame coal outburst prediction (cutting desorption) index critical value. This value can provide a theoretical basis for outburst prediction and prevention of low rank coal mines and similar occurrence conditions

  13. Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

    DOE PAGESBeta

    Sen, Satyabrata

    2015-08-04

    We develop space-time adaptive processing (STAP) methods by leveraging the advantages of sparse signal processing techniques in order to detect a slowly-moving target. We observe that the inherent sparse characteristics of a STAP problem can be formulated as the low-rankness of clutter covariance matrix when compared to the total adaptive degrees-of-freedom, and also as the sparse interference spectrum on the spatio-temporal domain. By exploiting these sparse properties, we propose two approaches for estimating the interference covariance matrix. In the first approach, we consider a constrained matrix rank minimization problem (RMP) to decompose the sample covariance matrix into a low-rank positivemore » semidefinite and a diagonal matrix. The solution of RMP is obtained by applying the trace minimization technique and the singular value decomposition with matrix shrinkage operator. Our second approach deals with the atomic norm minimization problem to recover the clutter response-vector that has a sparse support on the spatio-temporal plane. We use convex relaxation based standard sparse-recovery techniques to find the solutions. With extensive numerical examples, we demonstrate the performances of proposed STAP approaches with respect to both the ideal and practical scenarios, involving Doppler-ambiguous clutter ridges, spatial and temporal decorrelation effects. As a result, the low-rank matrix decomposition based solution requires secondary measurements as many as twice the clutter rank to attain a near-ideal STAP performance; whereas the spatio-temporal sparsity based approach needs a considerably small number of secondary data.« less

  14. Effect of Water Invasion on Outburst Predictive Index of Low Rank Coals in Dalong Mine

    PubMed Central

    Jiang, Jingyu; Cheng, Yuanping; Mou, Junhui; Jin, Kan; Cui, Jie

    2015-01-01

    To improve the coal permeability and outburst prevention, coal seam water injection and a series of outburst prevention measures were tested in outburst coal mines. These methods have become important technologies used for coal and gas outburst prevention and control by increasing the external moisture of coal or decreasing the stress of coal seam and changing the coal pore structure and gas desorption speed. In addition, techniques have had a significant impact on the gas extraction and outburst prevention indicators of coal seams. Globally, low rank coals reservoirs account for nearly half of hidden coal reserves and the most obvious feature of low rank coal is the high natural moisture content. Moisture will restrain the gas desorption and will affect the gas extraction and accuracy of the outburst prediction of coals. To study the influence of injected water on methane desorption dynamic characteristics and the outburst predictive index of coal, coal samples were collected from the Dalong Mine. The methane adsorption/desorption test was conducted on coal samples under conditions of different injected water contents. Selective analysis assessed the variations of the gas desorption quantities and the outburst prediction index (coal cutting desorption index). Adsorption tests indicated that the Langmuir volume of the Dalong coal sample is ~40.26 m3/t, indicating a strong gas adsorption ability. With the increase of injected water content, the gas desorption amount of the coal samples decreased under the same pressure and temperature. Higher moisture content lowered the accumulation desorption quantity after 120 minutes. The gas desorption volumes and moisture content conformed to a logarithmic relationship. After moisture correction, we obtained the long-flame coal outburst prediction (cutting desorption) index critical value. This value can provide a theoretical basis for outburst prediction and prevention of low rank coal mines and similar occurrence conditions

  15. Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

    SciTech Connect

    Sen, Satyabrata

    2015-08-04

    We develop space-time adaptive processing (STAP) methods by leveraging the advantages of sparse signal processing techniques in order to detect a slowly-moving target. We observe that the inherent sparse characteristics of a STAP problem can be formulated as the low-rankness of clutter covariance matrix when compared to the total adaptive degrees-of-freedom, and also as the sparse interference spectrum on the spatio-temporal domain. By exploiting these sparse properties, we propose two approaches for estimating the interference covariance matrix. In the first approach, we consider a constrained matrix rank minimization problem (RMP) to decompose the sample covariance matrix into a low-rank positive semidefinite and a diagonal matrix. The solution of RMP is obtained by applying the trace minimization technique and the singular value decomposition with matrix shrinkage operator. Our second approach deals with the atomic norm minimization problem to recover the clutter response-vector that has a sparse support on the spatio-temporal plane. We use convex relaxation based standard sparse-recovery techniques to find the solutions. With extensive numerical examples, we demonstrate the performances of proposed STAP approaches with respect to both the ideal and practical scenarios, involving Doppler-ambiguous clutter ridges, spatial and temporal decorrelation effects. As a result, the low-rank matrix decomposition based solution requires secondary measurements as many as twice the clutter rank to attain a near-ideal STAP performance; whereas the spatio-temporal sparsity based approach needs a considerably small number of secondary data.

  16. Production of hydrogen from low-rank coals: (Task 6. 1)

    SciTech Connect

    Sears, R.E.; Timpe, R.C.; Musich, M.A.; Cisney, S.J.

    1988-04-01

    The principal goal of this research project is to establish the feasibility of low-rank coal gasification for low-cost hydrogen production. This research involves a proof-of-concept and the early stages of engineering development using a continuous process unit (CPU). In parallel with this process development work, an evaluation of the relationship between the fundamental properties of low-rank coals and their reactivity under hydrogen-producing conditions is also being conducted. A 20--40 lb/hr fluid-bed gasifier (FBG) CPU was commissioned during this time period and has logged over 400 hours of operation during shakedown and operability testing. Maximum hydrogen production rates from the operability testing were over 17 SCF/lb MAF coal for both Wyodak and Velva test coals with a limestone bed, and for Martin Lake coal using 10 wt % trona, at 800{degree}C and a 2:1 steam:carbon ratio to 2:1 and increased with bed temperature over the range of 700{degree} to 800{degree}C. Agglomeration of the bed material when using trona as the catalyst was an operation problem during the CPU operability testing. The char of the low-rank coals was four to six times more reactive than that of the bituminous coal tested in the laboratory using thermogravimetric analysis (TGA). Surface analysis of the chars showed that the uniform distribution of K{sub 2}CO{sub 3} catalyst decreased with increasing coal rank. 8 refs., 17 figs., 16 tabs.

  17. On matrices with low-rank-plus-shift structure: Partial SVD and latent semantic indexing

    SciTech Connect

    Zha, H.; Zhang, Z.

    1998-08-01

    The authors present a detailed analysis of matrices satisfying the so-called low-rank-plus-shift property in connection with the computation of their partial singular value decomposition. The application they have in mind is Latent Semantic Indexing for information retrieval where the term-document matrices generated from a text corpus approximately satisfy this property. The analysis is motivated by developing more efficient methods for computing and updating partial SVD of large term-document matrices and gaining deeper understanding of the behavior of the methods in the presence of noise.

  18. Low-rank Quasi-Newton updates for Robust Jacobian lagging in Newton methods

    SciTech Connect

    Brown, J.; Brune, P.

    2013-07-01

    Newton-Krylov methods are standard tools for solving nonlinear problems. A common approach is to 'lag' the Jacobian when assembly or preconditioner setup is computationally expensive, in exchange for some degradation in the convergence rate and robustness. We show that this degradation may be partially mitigated by using the lagged Jacobian as an initial operator in a quasi-Newton method, which applies unassembled low-rank updates to the Jacobian until the next full reassembly. We demonstrate the effectiveness of this technique on problems in glaciology and elasticity. (authors)

  19. Parrallel Implementation of Fast Randomized Algorithms for Low Rank Matrix Decomposition

    SciTech Connect

    Lucas, Andrew J.; Stalizer, Mark; Feo, John T.

    2014-03-01

    We analyze the parallel performance of randomized interpolative decomposition by de- composing low rank complex-valued Gaussian random matrices larger than 100 GB. We chose a Cray XMT supercomputer as it provides an almost ideal PRAM model permitting quick investigation of parallel algorithms without obfuscation from hardware idiosyncrasies. We obtain that on non-square matrices performance scales almost linearly with runtime about 100 times faster on 128 processors. We also verify that numerically discovered error bounds still hold on matrices two orders of magnitude larger than those previously tested.

  20. Structural features of low rank coals with a tendency to spontaneous combustion

    SciTech Connect

    Artemov, A.V.; Saranchuk, V.I.; Semenenko, V.K.; Temerova, G.P.

    1983-01-01

    An investigation was made of the structural properties of low-rank Donets coals, including coals liable to spontaneous combustion. The coals showed marked differences in structure, elemental composition, aromaticity, organic sulphur and paramagnetic properties. During metamorphism, these structural differences tend to even out so that at the 80% carbon coalification stage, the chemical composition and structure of the coals susceptible to spontaneous combustion acquire a distinct similarity to those which are not susceptible, to the extent that the in-situ coal can no longer be considered self-heating.

  1. Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography

    NASA Astrophysics Data System (ADS)

    Kopriva, Ivica; Shi, Fei; Chen, Xinjian

    2016-07-01

    Speckle artifacts can strongly hamper quantitative analysis of optical coherence tomography (OCT), which is necessary to provide assessment of ocular disorders associated with vision loss. Here, we introduce a method for speckle reduction, which leverages from low-rank + sparsity decomposition (LRpSD) of the logarithm of intensity OCT images. In particular, we combine nonconvex regularization-based low-rank approximation of an original OCT image with a sparsity term that incorporates the speckle. State-of-the-art methods for LRpSD require a priori knowledge of a rank and approximate it with nuclear norm, which is not an accurate rank indicator. As opposed to that, the proposed method provides more accurate approximation of a rank through the use of nonconvex regularization that induces sparse approximation of singular values. Furthermore, a rank value is not required to be known a priori. This, in turn, yields an automatic and computationally more efficient method for speckle reduction, which yields the OCT image with improved contrast-to-noise ratio, contrast and edge fidelity. The source code will be available at www.mipav.net/English/research/research.html.

  2. Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection.

    PubMed

    Lang, Congyan; Feng, Jiashi; Feng, Songhe; Wang, Jingdong; Yan, Shuicheng

    2016-06-01

    Saliency detection is an important procedure for machines to understand visual world as humans do. In this paper, we consider a specific saliency detection problem of predicting human eye fixations when they freely view natural images, and propose a novel dual low-rank pursuit (DLRP) method. DLRP learns saliency-aware feature transformations by utilizing available supervision information and constructs discriminative bases for effectively detecting human fixation points under the popular low-rank and sparsity-pursuit framework. Benefiting from the embedded high-level information in the supervised learning process, DLRP is able to predict fixations accurately without performing the expensive object segmentation as in the previous works. Comprehensive experiments clearly show the superiority of the proposed DLRP method over the established state-of-the-art methods. We also empirically demonstrate that DLRP provides stronger generalization performance across different data sets and inherits the advantages of both the bottom-up- and top-down-based saliency detection methods. PMID:27046853

  3. Robust Low-Rank Tensor Recovery With Regularized Redescending M-Estimator.

    PubMed

    Yang, Yuning; Feng, Yunlong; Suykens, Johan A K

    2016-09-01

    This paper addresses the robust low-rank tensor recovery problems. Tensor recovery aims at reconstructing a low-rank tensor from some linear measurements, which finds applications in image processing, pattern recognition, multitask learning, and so on. In real-world applications, data might be contaminated by sparse gross errors. However, the existing approaches may not be very robust to outliers. To resolve this problem, this paper proposes approaches based on the regularized redescending M-estimators, which have been introduced in robust statistics. The robustness of the proposed approaches is achieved by the regularized redescending M-estimators. However, the nonconvexity also leads to a computational difficulty. To handle this problem, we develop algorithms based on proximal and linearized block coordinate descent methods. By explicitly deriving the Lipschitz constant of the gradient of the data-fitting risk, the descent property of the algorithms is present. Moreover, we verify that the objective functions of the proposed approaches satisfy the Kurdyka-Łojasiewicz property, which establishes the global convergence of the algorithms. The numerical experiments on synthetic data as well as real data verify that our approaches are robust in the presence of outliers and still effective in the absence of outliers. PMID:26302521

  4. Relationship between Particle Size Distribution of Low-Rank Pulverized Coal and Power Plant Performance

    DOE PAGESBeta

    Ganguli, Rajive; Bandopadhyay, Sukumar

    2012-01-01

    Tmore » he impact of particle size distribution (PSD) of pulverized, low rank high volatile content Alaska coal on combustion related power plant performance was studied in a series of field scale tests. Performance was gauged through efficiency (ratio of megawatt generated to energy consumed as coal), emissions (SO 2 , NO x , CO), and carbon content of ash (fly ash and bottom ash).he study revealed that the tested coal could be burned at a grind as coarse as 50% passing 76 microns, with no deleterious impact on power generation and emissions.he PSD’s tested in this study were in the range of 41 to 81 percent passing 76 microns.here was negligible correlation between PSD and the followings factors: efficiency, SO 2 , NO x , and CO. Additionally, two tests where stack mercury (Hg) data was collected, did not demonstrate any real difference in Hg emissions with PSD.he results from the field tests positively impacts pulverized coal power plants that burn low rank high volatile content coals (such as Powder River Basin coal).hese plants can potentially reduce in-plant load by grinding the coal less (without impacting plant performance on emissions and efficiency) and thereby, increasing their marketability.« less

  5. Low-rank approach for image nonblind deconvolution with variance estimation

    NASA Astrophysics Data System (ADS)

    Yang, Hang; Hu, Guosheng; Wang, Yuqing; Wu, Xiaotian

    2015-11-01

    We develop a low-rank approach for image restoration by exploiting the image's nonlocal self-similarity. We assume that the matrix stacked by the vectors of nonlocal similar patches is of low rank and has sparse singular values. Based on this assumption, we propose a new image deconvolution algorithm that decouples the deblurring and denoising steps. Specifically, in the deblurring step, we involve a regularized inversion of the blur in the Fourier domain, which amplifies and colors the noise and corrupts the image information. Hence, in the denoising step, a singular-value decomposition of similar packed patches is used to efficiently remove the colored noise. Furthermore, we derive an approach to update the estimation of noise variance for setting the threshold parameter at each iteration. Experimental results clearly show that the proposed algorithm outperforms many state-of-the-art deblurring algorithms such as iterative decoupled deblurring BM3D in terms of both improvement in signal-to-noise-ratio and visual perception quality.

  6. Performance of low-rank QR approximation of the finite element Biot-Savart law

    SciTech Connect

    White, D; Fasenfest, B

    2006-10-16

    In this paper we present a low-rank QR method for evaluating the discrete Biot-Savart law. Our goal is to develop an algorithm that is easily implemented on parallel computers. It is assumed that the known current density and the unknown magnetic field are both expressed in a finite element expansion, and we wish to compute the degrees-of-freedom (DOF) in the basis function expansion of the magnetic field. The matrix that maps the current DOF to the field DOF is full, but if the spatial domain is properly partitioned the matrix can be written as a block matrix, with blocks representing distant interactions being low rank and having a compressed QR representation. While an octree partitioning of the matrix may be ideal, for ease of parallel implementation we employ a partitioning based on number of processors. The rank of each block (i.e. the compression) is determined by the specific geometry and is computed dynamically. In this paper we provide the algorithmic details and present computational results for large-scale computations.

  7. Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation.

    PubMed

    Xu, Yong; Fang, Xiaozhao; Wu, Jian; Li, Xuelong; Zhang, David

    2016-02-01

    In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html. PMID:26701675

  8. Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection.

    PubMed

    Yang, Xi; Gao, Xinbo; Tao, Dacheng; Li, Xuelong; Han, Bing; Li, Jie

    2016-01-01

    An auroral substorm is an important geophysical phenomenon that reflects the interaction between the solar wind and the Earth's magnetosphere. Detecting substorms is of practical significance in order to prevent disruption to communication and global positioning systems. However, existing detection methods can be inaccurate or require time-consuming manual analysis and are therefore impractical for large-scale data sets. In this paper, we propose an automatic auroral substorm detection method based on a shape-constrained sparse and low-rank decomposition (SCSLD) framework. Our method automatically detects real substorm onsets in large-scale aurora sequences, which overcomes the limitations of manual detection. To reduce noise interference inherent in current SLD methods, we introduce a shape constraint to force the noise to be assigned to the low-rank part (stationary background), thus ensuring the accuracy of the sparse part (moving object) and improving the performance. Experiments conducted on aurora sequences in solar cycle 23 (1996-2008) show that the proposed SCSLD method achieves good performance for motion analysis of aurora sequences. Moreover, the obtained results are highly consistent with manual analysis, suggesting that the proposed automatic method is useful and effective in practice. PMID:25826810

  9. Liquefaction/solubilization of low-rank Turkish coals by white-rot fungus (Phanerochaete chrysosporium)

    SciTech Connect

    Elbeyli, I.Y.; Palantoken, A.; Piskin, S.; Kuzu, H.; Peksel, A.

    2006-08-15

    Microbial coal liquefaction/solubilization of three low-rank Turkish coals (Bursa-Kestelek, Kutahya-Seyitomer and Mugla-Yatagan lignite) was attempted by using a white-rot fungus (Phanerochaete chrysosporium DSM No. 6909); chemical compositions of the products were investigated. The lignite samples were oxidized by nitric acid under moderate conditions and then oxidized samples were placed on the agar medium of Phanerochaete chrysosporium. FTIR spectra of raw lignites, oxidized lignites and liquid products were recorded, and the acetone-soluble fractions of these samples were identified by GC-MS technique. Results show that the fungus affects the nitro and carboxyl/carbonyl groups in oxidized lignite sample, the liquid products obtained by microbial effects are the mixture of water-soluble compounds, and show limited organic solubility.

  10. Solving block linear systems with low-rank off-diagonal blocks is easily parallelizable

    SciTech Connect

    Menkov, V.

    1996-12-31

    An easily and efficiently parallelizable direct method is given for solving a block linear system Bx = y, where B = D + Q is the sum of a non-singular block diagonal matrix D and a matrix Q with low-rank blocks. This implicitly defines a new preconditioning method with an operation count close to the cost of calculating a matrix-vector product Qw for some w, plus at most twice the cost of calculating Qw for some w. When implemented on a parallel machine the processor utilization can be as good as that of those operations. Order estimates are given for the general case, and an implementation is compared to block SSOR preconditioning.

  11. Super-resolution images fusion via compressed sensing and low-rank matrix decomposition

    NASA Astrophysics Data System (ADS)

    Ren, Kan; Xu, Fuyuan

    2015-01-01

    Most of available image fusion approaches cannot achieve higher spatial resolution than the multisource images. In this paper we propose a novel simultaneous images super-resolution and fusion approach via the recently developed compressed sensing and multiscale dictionaries learning technology. Under the sparse prior of image patches and the framework of compressed sensing, multisource images fusion is reduced to a task of signal recovery from the compressive measurements. Then a set of multiscale dictionaries are learned from some groups of example high-resolution (HR) image patches via a nonlinear optimization algorithm. Moreover, a linear weights fusion rule is advanced to obtain the fused high-resolution image at each scale. Finally the high-resolution image is derived by performing a low-rank decomposition on the recovered high-resolution images at multiple scales. Some experiments are taken to investigate the performance of our proposed method, and the results prove its superiority to the counterparts.

  12. Desulfurization of low-rank Turkish coals by multi-gravity separator

    SciTech Connect

    Aydin, M.E.; Yildirim, I.; Dogan, M.Z.; Onal, G.; Celik, M.S.

    1996-12-31

    The Istanbul Region coals are characterized by high moisture contents (avg. 35%), high volatile matter values (avg. 45%), and more importantly high levels of sulfur in the range of 1 to 5%. These lignitic coals generally have relatively low ash (10%), and higher levels of calorific values over 5,000 Kcal/kg. The Multi-Gravity Separator (MGS), a new fine size gravity separation equipment, was tested to evaluate its potential for the desulfurization of these low-rank coals. Systematic tests conducted on two different samples of minus 1 mm size indicate that despite the finely distributed nature of coal and relatively small difference between coal and its associated gangue minerals, the degree of pyritic sulfur removal is 65.7% and 85.9% for the respective coals.

  13. Anaerobic processing of low-rank coals. Quarterly progress report, July 1--September 30, 1992

    SciTech Connect

    Jain, M.K.; Narayan, R.; Han, O.

    1992-12-31

    The overall goal of this project is to find biological methods to remove carboxylic functionalities from low-rank coals and to assess the properties of the modified coal towards coal liquefaction. The main objectives for this quarter were: (i) continuation of microbial consortia maintenance and completion of coal decarboxylation using batch reactor system, (ii) decarboxylation of model polymer, (iii) characterization of biotreated coals, and (iv) microautoclave liquefaction of the botreated coal. Progress is reported on the thermogravimetric analysis of coal biotreated in the absence of methanogens and under 5% hydrogen gas exhibits increased volatile carbon to fixed carbon ratio; that the microbial consortia developed on coal are being adapted to two different model polymers containing free carboxylic groups to examine decarboxylation ability of consortium; completion of experiments to decarboxylate two model polymers, polyacrylic acid and polymethyl methacrylate, have been completed; that the biotreated coal showed increase in THF-solubles.

  14. A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion

    NASA Astrophysics Data System (ADS)

    Pimentel-Alarcon, Daniel L.; Boston, Nigel; Nowak, Robert D.

    2016-06-01

    Low-rank matrix completion (LRMC) problems arise in a wide variety of applications. Previous theory mainly provides conditions for completion under missing-at-random samplings. An incomplete $d \\times N$ matrix is $\\textit{finitely completable}$ if there are at most finitely many rank-$r$ matrices that agree with all its observed entries. Finite completability is the tipping point in LRMC, as a few additional samples of a finitely completable matrix guarantee its $\\textit{unique}$ completability. The main contribution of this paper is a full characterization of finitely completable observation sets. We use this characterization to derive sufficient deterministic sampling conditions for unique completability. We also show that under uniform random sampling schemes, these conditions are satisfied with high probability if at least $\\mathscr{O}(\\max\\{r,\\log d \\})$ entries per column are observed.

  15. Anaerobic bioprocessing of low-rank coals. Progress report, April 1--June 30, 1992

    SciTech Connect

    Jain, M.K.; Narayan, R.; Han, O.

    1992-07-14

    We are seeking to find biological methods to remove carboxylic functionalities from low-rank coals and to assess the properties of the modified coal towards coal liquefaction. The main objectives for this quarter were : continuation of microbial consortia development and maintenance, evaluation of commercial decarboxylase, decarboxylation of lignite, demineralized Wyodak coal and model polymer, and characterization of biotreated coals. Specifically we report that two batch fermentor systems were completed and three other fermentors under optimum conditions for coal decarboxylation are in progress; that inhibition of growth of methanogens in the batch fermentor system enhanced the carbon dioxide production; that adapted microbial consortium produced more gas from lignite than Wyodak subbituminous coal; that phenylalanine decarboxylase exhibited insignificant coal decarboxylation activity; that two different microbial consortia developed on coal seem to be effective in decarboxylation of a polymer containing free carboxylic groups; and that CHN analyses of additional biotreated coals reconfirm increase in H/C ratio by 3--6%.

  16. Investigation of pyrolysis kinetics of humic acids from low rank Anatolian coal by thermal analysis

    SciTech Connect

    Tonbul, Y.; Erdogan, S.

    2007-07-01

    Thermogravimetric analysis (TGA) of humic acid samples from low rank Anatolian (east of Turkey, Bingol) coal were investigated under atmospheric pressure. The samples were subjected for the decomposition of organic matter ambient to 800{sup o} C at four different heating rates (5, 10, 15, and 20 degrees C min{sup -1}). The humic acid samples were started at decomposition between 170 - 206{sup o}C and amount of residues varied 55-60% according to heating rate. Each of samples showed a single step mass loss. TG/DTG data of samples were analyzed to determine activation energy values by Coats and Redfern method and Arrhenius method. Activation energy values are similar obtained from Coats and Redfern method and Arrhenius method and varied from 25 to 29 kJ mol{sup -1}.

  17. Improvement of thermal properties of low-rank coals treated by hydrothermal process

    SciTech Connect

    Xie, X.F.; Ohki, A.; Maeda, S.

    1999-07-01

    Australian low-rank coals, Loy Yang coal, Yallourn coal and Indonesian Adaro coal are hydrothermally treated at 200-350 C. The simultaneous TG/DTA is used to investigate the thermal properties, which include the volatile release profile under a nitrogen atmosphere and the burning profile under an air atmosphere. It is found that the temperature of volatile matter combustion (Ti1) of the hot water dried coals (upgraded coals) increases with heat treatment temperature (HTT), whereas the temperature of char combustion (Ti2), the temperature of maximum reaction (Tmax) and the temperature of char burn out (Tout) do not have large increase on the HTT. These results suggest that the HWD process can raise the volatile matter ignition temperature, resulting in improving the spontaneous ignition temperature, but it still maintains the original combustion behavior. Results from TG-DTA measurements are consistent with those determined by FTIR and solid state {sup 13}C CP/MAS NMR.

  18. Liquid CO{sub 2}/Coal Slurry for Feeding Low Rank Coal to Gasifiers

    SciTech Connect

    Marasigan, Jose; Goldstein, Harvey; Dooher, John

    2013-09-30

    This study investigates the practicality of using a liquid CO{sub 2}/coal slurry preparation and feed system for the E-Gas™ gasifier in an integrated gasification combined cycle (IGCC) electric power generation plant configuration. Liquid CO{sub 2} has several property differences from water that make it attractive for the coal slurries used in coal gasification-based power plants. First, the viscosity of liquid CO{sub 2} is much lower than water. This means it should take less energy to pump liquid CO{sub 2} through a pipe compared to water. This also means that a higher solids concentration can be fed to the gasifier, which should decrease the heat requirement needed to vaporize the slurry. Second, the heat of vaporization of liquid CO{sub 2} is about 80% lower than water. This means that less heat from the gasification reactions is needed to vaporize the slurry. This should result in less oxygen needed to achieve a given gasifier temperature. And third, the surface tension of liquid CO{sub 2} is about 2 orders of magnitude lower than water, which should result in finer atomization of the liquid CO{sub 2} slurry, faster reaction times between the oxygen and coal particles, and better carbon conversion at the same gasifier temperature. EPRI and others have recognized the potential that liquid CO{sub 2} has in improving the performance of an IGCC plant and have previously conducted systemslevel analyses to evaluate this concept. These past studies have shown that a significant increase in IGCC performance can be achieved with liquid CO{sub 2} over water with certain gasifiers. Although these previous analyses had produced some positive results, they were still based on various assumptions for liquid CO{sub 2}/coal slurry properties. This low-rank coal study extends the existing knowledge base to evaluate the liquid CO{sub 2}/coal slurry concept on an E-Gas™-based IGCC plant with full 90% CO{sub 2} capture. The overall objective is to determine if this

  19. Research Symposium I

    NASA Technical Reports Server (NTRS)

    2004-01-01

    The proceedings of this symposium consist of abstracts of talks presented by interns at NASA Glenn Research Center (GRC). The interns assisted researchers at GRC in projects which primarily address the following topics: aircraft engines and propulsion, spacecraft propulsion, fuel cells, thin film photovoltaic cells, aerospace materials, computational fluid dynamics, aircraft icing, management, and computerized simulation.

  20. Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data.

    PubMed

    Veganzones, Miguel A; Simoes, Miguel; Licciardi, Giorgio; Yokoya, Naoto; Bioucas-Dias, Jose M; Chanussot, Jocelyn

    2016-01-01

    Remote sensing hyperspectral images (HSIs) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low-dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, i.e., larger than the number of multispectral bands, the performance of these methods mainly decreases because the underlying sparse regression problem is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSIs are locally low rank, that is, pixels acquired from a given spatial neighborhood span a very low-dimensional subspace/manifold, i.e., lower or equal than the number of multispectral bands. Thus, we propose to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough, such that the problem is not ill-posed anymore. We propose two alternative approaches to define the hyperspectral super-resolution through local dictionary learning using endmember induction algorithms. We also explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approaches is illustrated with synthetic and semi real data. PMID:26540685

  1. Fifteenth symposium on biotechnology for fuels and chemicals: Program and abstracts

    SciTech Connect

    Not Available

    1993-07-01

    This collection contains 173 abstracts from presented papers and poster sessions. The five sessions of the conference were on the subjects of: (1) Thermal, Chemical, and Biological Processing, (2) Applied Biological Research, (3) Bioprocessing Research (4), Process Economics and Commercialization, and (5) Environmental Biotechnology. Examples of specific topics in the first session include the kinetics of ripening cheese, microbial liquefaction of lignite, and wheat as a feedstock for fuel ethanol. Typical topics in the second session were synergism studies of bacterial and fungal celluloses, conversion of inulin from jerusalem artichokes to sorbitol and ethanol by saccharomyces cerevisiae, and microbial conversion of high rank coals to methane. The third session entertained topics such as hydrodynamic modeling of a liquid fluidized bed bioreactor for coal biosolubilization, aqueous biphasic systems for biological particle partitioning, and arabinose utilization by xylose-fermenting yeast and fungi. The fourth session included such topics as silage processing of forage biomass to alcohol fuels, economics of molasses to ethanol in India, and production of lactic acid from renewable resources. the final session contained papers on such subjects as bioluminescent detection of contaminants in soils, characterization of petroleum contaminated soils in coral atolls in the south Pacific, and landfill management for methane generation and emission control.

  2. A Novel Fixed Low-Rank Constrained EEG Spatial Filter Estimation with Application to Movie-Induced Emotion Recognition

    PubMed Central

    Suyama, Takayuki

    2016-01-01

    This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a “bottom-up” manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. The advantage of the proposed method is that it combines feature selection, feature extraction, and classification into a monolithic optimization problem with a fixed low-rank regularization, which implicitly estimates optimal spatial filters. The proposed method shows competitive performance against the best CSP-based alternatives. PMID:27597862

  3. A Novel Fixed Low-Rank Constrained EEG Spatial Filter Estimation with Application to Movie-Induced Emotion Recognition.

    PubMed

    Yano, Ken; Suyama, Takayuki

    2016-01-01

    This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a "bottom-up" manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. The advantage of the proposed method is that it combines feature selection, feature extraction, and classification into a monolithic optimization problem with a fixed low-rank regularization, which implicitly estimates optimal spatial filters. The proposed method shows competitive performance against the best CSP-based alternatives. PMID:27597862

  4. Chemical and physical characterization of western low-rank-coal waste materials

    SciTech Connect

    Thompson, Carol May

    1981-03-01

    Evaluations of disposal requirements for solid wastes from power stations burning low-rank western coals is the primary objective of this program. Solid wastes to be characterized include: fly ashes, sludges from wet scrubbers, solids from fluidized bed combustion (FBC) processes and solids from dry scrubbing systems. Fly ashes and sludges to be studied will be obtained primarily from systems using alkaline fly ashes as significant sources of alkalinity for sulfur dioxide removal. Fluidized bed combustion wastes will include those produced by burning North Dakota lignite and Texas lignite. Dry scrubbing wastes will include those from spray drying systems and dry injection systems. Spray dryer wastes will be from a system using sodium carbonate as the scrubbing reagent. Dry injection wastes will come from systems using nahcolite and trona as sorbents. Spray dryer wastes, dry injection wastes, and FBC wastes will be supplied by the Grand Forks Energy Technology Center. Sludges and other samples will be collected at power stations using fly ash to supply alkalinity to wet scrubbers for sulfur dioxide removal. Sludges will be subjected to commercial fixation processes. Coal, fly ashes, treated and untreated sludges, scrubber liquor, FBC wastes, and dry scrubbing wastes will be subjected to a variety of chemical and physical tests. Results of these tests will be used to evaluate disposal requirements for wastes frm the systems studied.

  5. Electricity Market Forecasting via Low-Rank Multi-Kernel Learning

    NASA Astrophysics Data System (ADS)

    Kekatos, Vassilis; Zhang, Yu; Giannakis, Georgios B.

    2014-12-01

    The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure. Aligned to this end, modern statistical learning tools are leveraged here for electricity market inference. Day-ahead price forecasting is cast as a low-rank kernel learning problem. Uniquely exploiting the market clearing process, congestion patterns are modeled as rank-one components in the matrix of spatio-temporally varying prices. Through a novel nuclear norm-based regularization, kernels across pricing nodes and hours can be systematically selected. Even though market-wide forecasting is beneficial from a learning perspective, it involves processing high-dimensional market data. The latter becomes possible after devising a block-coordinate descent algorithm for solving the non-convex optimization problem involved. The algorithm utilizes results from block-sparse vector recovery and is guaranteed to converge to a stationary point. Numerical tests on real data from the Midwest ISO (MISO) market corroborate the prediction accuracy, computational efficiency, and the interpretative merits of the developed approach over existing alternatives.

  6. Low-rank separated representation surrogates of high-dimensional stochastic functions: Application in Bayesian inference

    SciTech Connect

    Validi, AbdoulAhad

    2014-03-01

    This study introduces a non-intrusive approach in the context of low-rank separated representation to construct a surrogate of high-dimensional stochastic functions, e.g., PDEs/ODEs, in order to decrease the computational cost of Markov Chain Monte Carlo simulations in Bayesian inference. The surrogate model is constructed via a regularized alternative least-square regression with Tikhonov regularization using a roughening matrix computing the gradient of the solution, in conjunction with a perturbation-based error indicator to detect optimal model complexities. The model approximates a vector of a continuous solution at discrete values of a physical variable. The required number of random realizations to achieve a successful approximation linearly depends on the function dimensionality. The computational cost of the model construction is quadratic in the number of random inputs, which potentially tackles the curse of dimensionality in high-dimensional stochastic functions. Furthermore, this vector-valued separated representation-based model, in comparison to the available scalar-valued case, leads to a significant reduction in the cost of approximation by an order of magnitude equal to the vector size. The performance of the method is studied through its application to three numerical examples including a 41-dimensional elliptic PDE and a 21-dimensional cavity flow.

  7. L₁-Norm Low-Rank Matrix Decomposition by Neural Networks and Mollifiers.

    PubMed

    Liu, Yiguang; Yang, Songfan; Wu, Pengfei; Li, Chunguang; Yang, Menglong

    2016-02-01

    The L1-norm cost function of the low-rank approximation of the matrix with missing entries is not smooth, and also cannot be transformed into a standard linear or quadratic programming problem, and thus, the optimization of this cost function is still not well solved. To tackle this problem, first, a mollifier is used to smooth the cost function. High closeness of the smoothed function to the original one can be obtained by tuning the parameters contained in the mollifier. Next, a recurrent neural network is proposed to optimize the mollified function, which will converge to a local minimum. In addition, to boost the speed of the system, the mollifying process is implemented by a filtering procedure. The influence of two mollifier parameters is theoretically analyzed and experimentally confirmed, showing that one of the parameters is critical to computational efficiency and accuracy, while the other not. A large number of experiments on synthetic data show that the proposed method is competitive to the state-of-the-art methods. In particular, the experiments on large matrices and a real application in the structure from motion indicate that the memory requirement of the proposed algorithm is mild, making it suitable for real applications that often involve large-scale matrix decomposition. PMID:26595933

  8. Thermolysis of a polymer model of aromatic carboxylic acids in low-rank coal

    SciTech Connect

    Mungall, W.S.; Britt, P.F.; Buchanan, A.C. III

    1997-03-01

    To compliment our current investigation into the role that decarboxylation of aromatic carboxylic acids plays in the low-temperature cross-linking of low-rank coals, we are investigating the thermolysis of a polymeric coal model compound to determine if the polymeric network structure of coal can alter the decarboxylation pathways. In this investigation, a bibenzylic polymer, poly-(m-xylylene-co-5-carboxy-m-xylylene), 1, was synthesized containing 2.3 carboxylic acids per 100 carbons, which is similar to that found in Zapp lignite. The pyrolysis of 1 was compared to poly-m-xylylene, 2, and the methyl ester of 1, 3, to determine if the carboxy group enhances cross-linking reactions. The major product from the pyrolysis of 1 at 375{degrees} C or 400{degrees} C for 1 h was a THF insoluble residue (60-75 wt%), while pyrolysis of 2 or the methyl ester of 1 produced only a THF soluble product. The mechanistic pathways leading to cross-linking will be discussed.

  9. Computer molecular models of low-rank coal and char containing inorganic complexes.

    PubMed

    Domazetis, George; James, Bruce D; Liesegang, John

    2008-07-01

    Molecular models of low-rank coal containing water, aqua-ionic species, and transition metal aqua-complexes, were optimised using semi-empirical (SE) quantum mechanics; the model was constructed with properties similar to brown coal; 10-20 wt% water was hydrogen bonded to coal oxygen groups, and the remainder was bulk water. Single point self-consistent field (1scf) computations of coal models provided octahedral mono-, and di-nuclear complexes of Cr, Fe, Co, and Ni, but SE computations often provided distorted structures. Models of char were developed by transforming the coal model containing multi-nuclear metal species into char according to pyrolysis chemistry; the composition of char models containing iron oxides was similar to char samples obtained over 250-800 degrees C. Density functional theory (DFT) optimisation of char models with metal clusters provided low energy configurations of disordered structures with a shallow energy minimum. SE and DFT calculations of char models containing metal clusters were conducted for mechanisms for H2 and CO formation from pyrolysis and iron-catalysed steam gasification; the active site for gasification was [Fe-C] and its accessibility to H2O was related to the configuration of the char model. The major steps in iron-catalysed steam gasification were chemi-adsorption of water on [Fe-C], hydrogen abstraction, and oxygen transfer. PMID:18478281

  10. Distributed Compressive Sensing of Hyperspectral Images Using Low Rank and Structure Similarity Property

    NASA Astrophysics Data System (ADS)

    Huang, Bingchao; Xu, Ke; Wan, Jianwei; Liu, Xu

    2015-11-01

    An efficient method and system for distributed compressive sensing of hyperspectral images is presented, which exploit the low rank and structure similarity property of hyperspectral imagery. In this paper, by integrating the respective characteristics of DSC and CS, a distributed compressive sensing framework is proposed to simultaneously capture and compress hyperspectral images. At the encoder, every band image is measured independently, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. It is simple to operate and easy to hardware implementation. At the decoder, each band image is reconstructed by the method of total variation norm minimize. During each band reconstruction, the low rand structure of band images and spectrum structure similarity are used to give birth to the new regularizers. With combining the new regularizers and other regularizer, we can sufficiently exploit the spatial correlation, spectral correlation and spectral structural redundancy in hyperspectral imagery. A numerical optimization algorithm is also proposed to solve the reconstruction model by augmented Lagrangian multiplier method. Experimental results show that this method can effectively improve the reconstruction quality of hyperspectral images.

  11. A low-rank approximation-based transductive support tensor machine for semisupervised classification.

    PubMed

    Liu, Xiaolan; Guo, Tengjiao; He, Lifang; Yang, Xiaowei

    2015-06-01

    In the fields of machine learning, pattern recognition, image processing, and computer vision, the data are usually represented by the tensors. For the semisupervised tensor classification, the existing transductive support tensor machine (TSTM) needs to resort to iterative technique, which is very time-consuming. In order to overcome this shortcoming, in this paper, we extend the concave-convex procedure-based transductive support vector machine (CCCP-TSVM) to the tensor patterns and propose a low-rank approximation-based TSTM, in which the tensor rank-one decomposition is used to compute the inner product of the tensors. Theoretically, concave-convex procedure-based TSTM (CCCP-TSTM) is an extension of the linear CCCP-TSVM to tensor patterns. When the input patterns are vectors, CCCP-TSTM degenerates into the linear CCCP-TSVM. A set of experiments is conducted on 23 semisupervised classification tasks, which are generated from seven second-order face data sets, three third-order gait data sets, and two third-order image data sets, to illustrate the performance of the CCCP-TSTM. The results show that compared with CCCP-TSVM and TSTM, CCCP-TSTM provides significant performance gain in terms of test accuracy and training speed. PMID:25700447

  12. Fast dynamic electron paramagnetic resonance (EPR) oxygen imaging using low-rank tensors.

    PubMed

    Christodoulou, Anthony G; Redler, Gage; Clifford, Bryan; Liang, Zhi-Pei; Halpern, Howard J; Epel, Boris

    2016-09-01

    Hypoxic tumors are resistant to radiotherapy, motivating the development of tools to image local oxygen concentrations. It is generally believed that stable or chronic hypoxia is the source of resistance, but more recent work suggests a role for transient hypoxia. Conventional EPR imaging (EPRI) is capable of imaging tissue pO2in vivo, with high pO2 resolution and 1mm spatial resolution but low imaging speed (10min temporal resolution for T1-based pO2 mapping), which makes it difficult to investigate the oxygen changes, e.g., transient hypoxia. Here we describe a new imaging method which accelerates dynamic EPR oxygen imaging, allowing 3D imaging at 2 frames per minute, fast enough to image transient hypoxia at the "speed limit" of observed pO2 change. The method centers on a low-rank tensor model that decouples the tradeoff between imaging speed, spatial coverage/resolution, and number of inversion times (pO2 accuracy). We present a specialized sparse sampling strategy and image reconstruction algorithm for use with this model. The quality and utility of the method is demonstrated in simulations and in vivo experiments in tumor bearing mice. PMID:27498337

  13. k-t FASTER: Acceleration of functional MRI data acquisition using low rank constraints

    PubMed Central

    Chiew, Mark; Smith, Stephen M; Koopmans, Peter J; Graedel, Nadine N; Blumensath, Thomas; Miller, Karla L

    2015-01-01

    Purpose In functional MRI (fMRI), faster sampling of data can provide richer temporal information and increase temporal degrees of freedom. However, acceleration is generally performed on a volume-by-volume basis, without consideration of the intrinsic spatio-temporal data structure. We present a novel method for accelerating fMRI data acquisition, k-t FASTER (FMRI Accelerated in Space-time via Truncation of Effective Rank), which exploits the low-rank structure of fMRI data. Theory and Methods Using matrix completion, 4.27× retrospectively and prospectively under-sampled data were reconstructed (coil-independently) using an iterative nonlinear algorithm, and compared with several different reconstruction strategies. Matrix reconstruction error was evaluated; a dual regression analysis was performed to determine fidelity of recovered fMRI resting state networks (RSNs). Results The retrospective sampling data showed that k-t FASTER produced the lowest error, approximately 3–4%, and the highest quality RSNs. These results were validated in prospectively under-sampled experiments, with k-t FASTER producing better identification of RSNs than fully sampled acquisitions of the same duration. Conclusion With k-t FASTER, incoherently under-sampled fMRI data can be robustly recovered using only rank constraints. This technique can be used to improve the speed of fMRI sampling, particularly for multivariate analyses such as temporal independent component analysis. Magn Reson Med 74:353–364, 2015. © 2014 Wiley Periodicals, Inc. PMID:25168207

  14. Characteristics and Thermal Behaviour of Low Rank Malaysian Coals towards Liquefaction Performance via Thermogravimetric Analysis

    NASA Astrophysics Data System (ADS)

    Ishak, M. A. M.; Ismail, K.; Nawawi, W. I.; Jawad, A. H.; Abdullah, M. F.; Kasim, M. N.; Ani, A. Y.

    2016-07-01

    In this study, thermal behaviour of two low-rank Malaysian coals namely Mukah Balingian (MB) and Batu Arang (BA) were obtained under pyrolysis conditions via Thermogravimetric analysis (TGA) at a heating rate of 20°C min-1. The thermal characteristics of the coals were investigated prior to direct liquefaction in order to determine the liquefaction performance, i.e. coal conversion and oil yield. The differential weight loss (DTG) results for both coals showed that there are three main stages evolved which consists of moisture, volatile matter and heavier hydrocarbons that correspond to temperature range of 150, 200-500 and 550-800°C, respectively. Apparently, the DTG curves of BA coal reveals a similar pattern of thermal evolution profile in comparison to that of the MB coal. However, the calculated mean reactivity of BA coal is higher than that of MB, which implied that BA would probably enhance coal conversion and oil yield in comparison to MB coal. Interestingly, results showed that under the same liquefaction conditions (i.e. at 4MPa pressure and 420°C), conversion and oil yield of both coals were well correlated with their reactivity and petrofactor value obtained.

  15. Ultra low radiation dose digital subtraction angiography (DSA) imaging using low rank constraint

    NASA Astrophysics Data System (ADS)

    Niu, Kai; Li, Yinsheng; Schafer, Sebastian; Royalty, Kevin; Wu, Yijing; Strother, Charles; Chen, Guang-Hong

    2015-03-01

    In this work we developed a novel denoising algorithm for DSA image series. This algorithm takes advantage of the low rank nature of the DSA image sequences to enable a dramatic reduction in radiation and/or contrast doses in DSA imaging. Both spatial and temporal regularizers were introduced in the optimization algorithm to further reduce noise. To validate the method, in vivo animal studies were conducted with a Siemens Artis Zee biplane system using different radiation dose levels and contrast concentrations. Both conventionally processed DSA images and the DSA images generated using the novel denoising method were compared using absolute noise standard deviation and the contrast to noise ratio (CNR). With the application of the novel denoising algorithm for DSA, image quality can be maintained with a radiation dose reduction by a factor of 20 and/or a factor of 2 reduction in contrast dose. Image processing is completed on a GPU within a second for a 10s DSA data acquisition.

  16. Cardiac diffusion tensor imaging based on compressed sensing using joint sparsity and low-rank approximation.

    PubMed

    Huang, Jianping; Wang, Lihui; Chu, Chunyu; Zhang, Yanli; Liu, Wanyu; Zhu, Yuemin

    2016-04-29

    Diffusion tensor magnetic resonance (DTMR) imaging and diffusion tensor imaging (DTI) have been widely used to probe noninvasively biological tissue structures. However, DTI suffers from long acquisition times, which limit its practical and clinical applications. This paper proposes a new Compressed Sensing (CS) reconstruction method that employs joint sparsity and rank deficiency to reconstruct cardiac DTMR images from undersampled k-space data. Diffusion-weighted images acquired in different diffusion directions were firstly stacked as columns to form the matrix. The matrix was row sparse in the transform domain and had a low rank. These two properties were then incorporated into the CS reconstruction framework. The underlying constrained optimization problem was finally solved by the first-order fast method. Experiments were carried out on both simulation and real human cardiac DTMR images. The results demonstrated that the proposed approach had lower reconstruction errors for DTI indices, including fractional anisotropy (FA) and mean diffusivities (MD), compared to the existing CS-DTMR image reconstruction techniques. PMID:27163322

  17. Conflict-cost based random sampling design for parallel MRI with low rank constraints

    NASA Astrophysics Data System (ADS)

    Kim, Wan; Zhou, Yihang; Lyu, Jingyuan; Ying, Leslie

    2015-05-01

    In compressed sensing MRI, it is very important to design sampling pattern for random sampling. For example, SAKE (simultaneous auto-calibrating and k-space estimation) is a parallel MRI reconstruction method using random undersampling. It formulates image reconstruction as a structured low-rank matrix completion problem. Variable density (VD) Poisson discs are typically adopted for 2D random sampling. The basic concept of Poisson disc generation is to guarantee samples are neither too close to nor too far away from each other. However, it is difficult to meet such a condition especially in the high density region. Therefore the sampling becomes inefficient. In this paper, we present an improved random sampling pattern for SAKE reconstruction. The pattern is generated based on a conflict cost with a probability model. The conflict cost measures how many dense samples already assigned are around a target location, while the probability model adopts the generalized Gaussian distribution which includes uniform and Gaussian-like distributions as special cases. Our method preferentially assigns a sample to a k-space location with the least conflict cost on the circle of the highest probability. To evaluate the effectiveness of the proposed random pattern, we compare the performance of SAKEs using both VD Poisson discs and the proposed pattern. Experimental results for brain data show that the proposed pattern yields lower normalized mean square error (NMSE) than VD Poisson discs.

  18. Fast dynamic electron paramagnetic resonance (EPR) oxygen imaging using low-rank tensors

    NASA Astrophysics Data System (ADS)

    Christodoulou, Anthony G.; Redler, Gage; Clifford, Bryan; Liang, Zhi-Pei; Halpern, Howard J.; Epel, Boris

    2016-09-01

    Hypoxic tumors are resistant to radiotherapy, motivating the development of tools to image local oxygen concentrations. It is generally believed that stable or chronic hypoxia is the source of resistance, but more recent work suggests a role for transient hypoxia. Conventional EPR imaging (EPRI) is capable of imaging tissue pO2in vivo, with high pO2 resolution and 1 mm spatial resolution but low imaging speed (10 min temporal resolution for T1-based pO2 mapping), which makes it difficult to investigate the oxygen changes, e.g., transient hypoxia. Here we describe a new imaging method which accelerates dynamic EPR oxygen imaging, allowing 3D imaging at 2 frames per minute, fast enough to image transient hypoxia at the "speed limit" of observed pO2 change. The method centers on a low-rank tensor model that decouples the tradeoff between imaging speed, spatial coverage/resolution, and number of inversion times (pO2 accuracy). We present a specialized sparse sampling strategy and image reconstruction algorithm for use with this model. The quality and utility of the method is demonstrated in simulations and in vivo experiments in tumor bearing mice.

  19. Low-rank network decomposition reveals structural characteristics of small-world networks

    NASA Astrophysics Data System (ADS)

    Barranca, Victor J.; Zhou, Douglas; Cai, David

    2015-12-01

    Small-world networks occur naturally throughout biological, technological, and social systems. With their prevalence, it is particularly important to prudently identify small-world networks and further characterize their unique connection structure with respect to network function. In this work we develop a formalism for classifying networks and identifying small-world structure using a decomposition of network connectivity matrices into low-rank and sparse components, corresponding to connections within clusters of highly connected nodes and sparse interconnections between clusters, respectively. We show that the network decomposition is independent of node indexing and define associated bounded measures of connectivity structure, which provide insight into the clustering and regularity of network connections. While many existing network characterizations rely on constructing benchmark networks for comparison or fail to describe the structural properties of relatively densely connected networks, our classification relies only on the intrinsic network structure and is quite robust with respect to changes in connection density, producing stable results across network realizations. Using this framework, we analyze several real-world networks and reveal new structural properties, which are often indiscernible by previously established characterizations of network connectivity.

  20. Calibrationless Parallel Imaging Reconstruction Based on Structured Low-Rank Matrix Completion

    PubMed Central

    Shin, Peter J.; Larson, Peder E.Z.; Ohliger, Michael A.; Elad, Michael; Pauly, John M.; Vigneron, Daniel B.; Lustig, Michael

    2013-01-01

    Purpose A calibrationless parallel imaging reconstruction method, termed simultaneous auto-calibrating and k-space estimation (SAKE), is presented. It is a data-driven, coil-by-coil reconstruction method that does not require a separate calibration step for estimating coil sensitivity information. Methods In SAKE, an under-sampled multi-channel dataset is structured into a single data matrix. Then the reconstruction is formulated as a structured low-rank matrix completion problem. An iterative solution that implements a projection-onto-sets algorithm with singular value thresholding is described. Results Reconstruction results are demonstrated for retrospectively and prospectively under-sampled, multi-channel Cartesian data having no calibration signals. Additionally, non-Cartesian data reconstruction is presented. Finally, improved image quality is demonstrated by combining SAKE with wavelet-based compressed sensing. Conclusion As estimation of coil sensitivity information is not needed, the proposed method could potentially benefit MR applications where acquiring accurate calibration data is limiting or not possible at all. PMID:24248734

  1. Lanczos-based Low-Rank Correction Method for Solving the Dyson Equation in Inhomogenous Dynamical Mean-Field Theory

    NASA Astrophysics Data System (ADS)

    Carrier, Pierre; Tang, Jok M.; Saad, Yousef; Freericks, James K.

    Inhomogeneous dynamical mean-field theory has been employed to solve many interesting strongly interacting problems from transport in multilayered devices to the properties of ultracold atoms in a trap. The main computational step, especially for large systems, is the problem of calculating the inverse of a large sparse matrix to solve Dyson's equation and determine the local Green's function at each lattice site from the corresponding local self-energy. We present a new e_cient algorithm, the Lanczos-based low-rank algorithm, for the calculation of the inverse of a large sparse matrix which yields this local (imaginary time) Green's function. The Lanczos-based low-rank algorithm is based on a domain decomposition viewpoint, but avoids explicit calculation of Schur complements and relies instead on low-rank matrix approximations derived from the Lanczos algorithm, for solving the Dyson equation. We report at least a 25-fold improvement of performance compared to explicit decomposition (such as sparse LU) of the matrix inverse. We also report that scaling relative to matrix sizes, of the low-rank correction method on the one hand and domain decomposition methods on the other, are comparable.

  2. Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations

    PubMed Central

    Niu, Shanzhou; Zhang, Shanli; Huang, Jing; Bian, Zhaoying; Chen, Wufan; Yu, Gaohang; Liang, Zhengrong; Ma, Jianhua

    2016-01-01

    Cerebral perfusion x-ray computed tomography (PCT) is an important functional imaging modality for evaluating cerebrovascular diseases and has been widely used in clinics over the past decades. However, due to the protocol of PCT imaging with repeated dynamic sequential scans, the associative radiation dose unavoidably increases as compared with that used in conventional CT examinations. Minimizing the radiation exposure in PCT examination is a major task in the CT field. In this paper, considering the rich similarity redundancy information among enhanced sequential PCT images, we propose a low-dose PCT image restoration model by incorporating the low-rank and sparse matrix characteristic of sequential PCT images. Specifically, the sequential PCT images were first stacked into a matrix (i.e., low-rank matrix), and then a non-convex spectral norm/regularization and a spatio-temporal total variation norm/regularization were then built on the low-rank matrix to describe the low rank and sparsity of the sequential PCT images, respectively. Subsequently, an improved split Bregman method was adopted to minimize the associative objective function with a reasonable convergence rate. Both qualitative and quantitative studies were conducted using a digital phantom and clinical cerebral PCT datasets to evaluate the present method. Experimental results show that the presented method can achieve images with several noticeable advantages over the existing methods in terms of noise reduction and universal quality index. More importantly, the present method can produce more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps. PMID:27440948

  3. Scoping Studies to Evaluate the Benefits of an Advanced Dry Feed System on the Use of Low-Rank Coal

    SciTech Connect

    Rader, Jeff; Aguilar, Kelly; Aldred, Derek; Chadwick, Ronald; Conchieri, John; Dara, Satyadileep; Henson, Victor; Leininger, Tom; Liber, Pawel; Liber, Pawel; Lopez-Nakazono, Benito; Pan, Edward; Ramirez, Jennifer; Stevenson, John; Venkatraman, Vignesh

    2012-03-30

    The purpose of this project was to evaluate the ability of advanced low rank coal gasification technology to cause a significant reduction in the COE for IGCC power plants with 90% carbon capture and sequestration compared with the COE for similarly configured IGCC plants using conventional low rank coal gasification technology. GE’s advanced low rank coal gasification technology uses the Posimetric Feed System, a new dry coal feed system based on GE’s proprietary Posimetric Feeder. In order to demonstrate the performance and economic benefits of the Posimetric Feeder in lowering the cost of low rank coal-fired IGCC power with carbon capture, two case studies were completed. In the Base Case, the gasifier was fed a dilute slurry of Montana Rosebud PRB coal using GE’s conventional slurry feed system. In the Advanced Technology Case, the slurry feed system was replaced with the Posimetric Feed system. The process configurations of both cases were kept the same, to the extent possible, in order to highlight the benefit of substituting the Posimetric Feed System for the slurry feed system.

  4. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

    PubMed Central

    Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

    2015-01-01

    Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

  5. CO{sub 2} SEQUESTRATION POTENTIAL OF TEXAS LOW-RANK COALS

    SciTech Connect

    Duane A. McVay; Walter B. Ayers Jr; Jerry L. Jensen

    2005-02-01

    The objectives of this project are to evaluate the feasibility of carbon dioxide (CO{sub 2}) sequestration in Texas low-rank coals and to determine the potential for enhanced coalbed methane (CBM) recovery as an added benefit of sequestration. There were three main objectives for this reporting period, which related to obtaining accurate parameters for reservoir model description and modeling reservoir performance of CO{sub 2} sequestration and enhanced coalbed methane recovery. The first objective was to collect and desorb gas from 10 sidewall core coal samples from an Anadarko Petroleum Corporation well (APCL2 well) at approximately 6,200-ft depth in the Lower Calvert Bluff Formation of the Wilcox Group in east-central Texas. The second objective was to measure sorptive capacities of these Wilcox coal samples for CO{sub 2}, CH{sub 4}, and N{sub 2}. The final objective was to contract a service company to perform pressure transient testing in Wilcox coal beds in a shut-in well, to determine permeability of deep Wilcox coal. Bulk density of the APCL2 well sidewall core samples averaged 1.332 g/cc. The 10 sidewall core samples were placed in 4 sidewall core canisters and desorbed. Total gas content of the coal (including lost gas and projected residual gas) averaged 395 scf/ton on an as-received basis. The average lost gas estimations were approximately 45% of the bulk sample total gas. Projected residual gas was 5% of in-situ gas content. Six gas samples desorbed from the sidewall cores were analyzed to determine gas composition. Average gas composition was approximately 94.3% methane, 3.0% ethane, and 0.7% propane, with traces of heavier hydrocarbon gases. Carbon dioxide averaged 1.7%. Coal from the 4 canisters was mixed to form one composite sample that was used for pure CO{sub 2}, CH{sub 4}, and N{sub 2} isotherm analyses. The composite sample was 4.53% moisture, 37.48% volatile matter, 9.86% ash, and 48.12% fixed carbon. Mean vitrinite reflectance was 0

  6. Advanced CO{sub 2} Capture Technology for Low Rank Coal IGCC System

    SciTech Connect

    Alptekin, Gokhan

    2013-09-30

    The overall objective of the project is to demonstrate the technical and economic viability of a new Integrated Gasification Combined Cycle (IGCC) power plant designed to efficiently process low rank coals. The plant uses an integrated CO{sub 2} scrubber/Water Gas Shift (WGS) catalyst to capture over90 percent capture of the CO{sub 2} emissions, while providing a significantly lower cost of electricity (COE) than a similar plant with conventional cold gas cleanup system based on SelexolTM technology and 90 percent carbon capture. TDA’s system uses a high temperature physical adsorbent capable of removing CO{sub 2} above the dew point of the synthesis gas and a commercial WGS catalyst that can effectively convert CO in The overall objective of the project is to demonstrate the technical and economic viability of a new Integrated Gasification Combined Cycle (IGCC) power plant designed to efficiently process low rank coals. The plant uses an integrated CO{sub 2} scrubber/Water Gas Shift (WGS) catalyst to capture over90 percent capture of the CO{sub 2} emissions, while providing a significantly lower cost of electricity (COE) than a similar plant with conventional cold gas cleanup system based on SelexolTM technology and 90 percent carbon capture. TDA’s system uses a high temperature physical adsorbent capable of removing CO{sub 2} above the dew point of the synthesis gas and a commercial WGS catalyst that can effectively convert CO in bituminous coal the net plant efficiency is about 2.4 percentage points higher than an Integrated Gasification Combined Cycle (IGCC) plant equipped with SelexolTM to capture CO{sub 2}. We also previously completed two successful field demonstrations: one at the National Carbon Capture Center (Southern- Wilsonville, AL) in 2011, and a second demonstration in fall of 2012 at the Wabash River IGCC plant (Terra Haute, IN). In this project, we first optimized the sorbent to catalyst ratio used in the combined WGS and CO{sub 2} capture

  7. Microbial and chemical factors influencing methane production in laboratory incubations of low-rank subsurface coals

    USGS Publications Warehouse

    Harris, Stephen H.; Smith, Richard L.; Barker, Charles E.

    2008-01-01

    Lignite and subbituminous coals were investigated for their ability to support microbial methane production in laboratory incubations. Results show that naturally-occurring microorganisms associated with the coals produced substantial quantities of methane, although the factors influencing this process were variable among different samples tested. Methanogenic microbes in two coals from the Powder River Basin, Wyoming, USA, produced 140.5-374.6 mL CH4/kg ((4.5-12.0 standard cubic feet (scf)/ton) in response to an amendment of H2/CO2. The addition of high concentrations (5-10 mM) of acetate did not support substantive methane production under the laboratory conditions. However, acetate accumulated in control incubations where methanogenesis was inhibited, indicating that acetate was produced and consumed during the course of methane production. Acetogenesis from H2/CO2 was evident in these incubations and may serve as a competing metabolic mode influencing the cumulative amount of methane produced in coal. Two low-rank (lignite A) coals from Fort Yukon, Alaska, USA, demonstrated a comparable level of methane production (131.1-284.0 mL CH4/kg (4.2-9.1 scf/ton)) in the presence of an inorganic nutrient amendment, indicating that the source of energy and organic carbon was derived from the coal. The concentration of chloroform-extractable organic matter varied by almost three orders of magnitude among all the coals tested, and appeared to be related to methane production potential. These results indicate that substrate availability within the coal matrix and competition between different groups of microorganisms are two factors that may exert a profound influence on methanogenesis in subsurface coal beds.

  8. Higher rank numerical ranges and low rank perturbations of quantum channels

    NASA Astrophysics Data System (ADS)

    Li, Chi-Kwong; Poon, Yiu-Tung; Sze, Nung-Sing

    2008-12-01

    For a positive integer k, the rank-k numerical range [Lambda]k(A) of an operator A acting on a Hilbert space of dimension at least k is the set of scalars [lambda] such that PAP=[lambda]P for some rank k orthogonal projection P. In this paper, a close connection between low rank perturbation of an operator A and [Lambda]k(A) is established. In particular, for 1[less-than-or-equals, slant]r

  9. Co-pyrolysis of low rank coals and biomass: Product distributions

    SciTech Connect

    Soncini, Ryan M; Means, Nicholas C; Weiland, Nathan T

    2013-10-01

    Pyrolysis and gasification of combined low rank coal and biomass feeds are the subject of much study in an effort to mitigate the production of green house gases from integrated gasification combined cycle (IGCC) systems. While co-feeding has the potential to reduce the net carbon footprint of commercial gasification operations, the effects of co-feeding on kinetics and product distributions requires study to ensure the success of this strategy. Southern yellow pine was pyrolyzed in a semi-batch type drop tube reactor with either Powder River Basin sub-bituminous coal or Mississippi lignite at several temperatures and feed ratios. Product gas composition of expected primary constituents (CO, CO{sub 2}, CH{sub 4}, H{sub 2}, H{sub 2}O, and C{sub 2}H{sub 4}) was determined by in-situ mass spectrometry while minor gaseous constituents were determined using a GC-MS. Product distributions are fit to linear functions of temperature, and quadratic functions of biomass fraction, for use in computational co-pyrolysis simulations. The results are shown to yield significant nonlinearities, particularly at higher temperatures and for lower ranked coals. The co-pyrolysis product distributions evolve more tar, and less char, CH{sub 4}, and C{sub 2}H{sub 4}, than an additive pyrolysis process would suggest. For lignite co-pyrolysis, CO and H{sub 2} production are also reduced. The data suggests that evolution of hydrogen from rapid pyrolysis of biomass prevents the crosslinking of fragmented aromatic structures during coal pyrolysis to produce tar, rather than secondary char and light gases. Finally, it is shown that, for the two coal types tested, co-pyrolysis synergies are more significant as coal rank decreases, likely because the initial structure in these coals contains larger pores and smaller clusters of aromatic structures which are more readily retained as tar in rapid co-pyrolysis.

  10. Investigation of mechanisms of ash deposit formation from low-rank coal combustion: Final report

    SciTech Connect

    Greene, F.T.; O'Donnell, J.E.

    1987-08-01

    This project was undertaken to determine the chemical behavior of alkali metal and other species implicated in the ash fouling which can occur during the combustion of low rank coals. The coal combustion was studied in unaugmented premixed pulverized coal flames. Vapor species were measured by molecular beam mass spectrometry. Temperatures were also measured, and time-resolved coal/ash particulate samples were collected and analyzed. A major part of the research on this project was devoted to: (1) the development and refinement of techniques for the MBMS analysis of trace quantities of unstable and reactive high temperature vapor species from the pulverized coal flames; and (2) the time-resolved sampling and collection of particulates. The equipment is now operating very satisfactorily. Inorganic species, some of which were present at parts-per-million levels, were quantitatively sampled and measured in the pulverized coal flames. Time-resolved particulate samples which were free of vapor deposited contaminants were collected without the use of an interfering substrate. Profiles of the alkali metal species in Beulah lignite and Decker subbituminous coal flames were obtained. It was found in both flames that sodium is volatilized as the atomic species early (milliseconds) in the combustion process. The gaseous Na reacts, also in milliseconds, to form an unknown species which is probably an oxide fume, but which is not NaOH or Na/sub 2/SO/sub 4/. This is probably the mechanism for the formation of the alkali ''fumes'' observed in other systems. Measurements were also made of a number of other gaseous species, and time-resolved coal/ash samples were obtained and analyzed. 27 refs., 23 figs., 8 tabs.

  11. A Symposium.

    ERIC Educational Resources Information Center

    Rachal, John R.

    2003-01-01

    Uses the framework of a symposium to present an imagined discussion by historical figures about whether and how knowledge might be acquired. Discussants include Democritus, Protagoras, Heraclitus, Socrates, Jesus, Gorgias, Nietzsche, Buddha, and Kierkegaard. (Contains 40 endnotes.) (SK)

  12. 1979 DOE statistical symposium

    SciTech Connect

    Gardiner, D.A.; Truett T.

    1980-09-01

    The 1979 DOE Statistical Symposium was the fifth in the series of annual symposia designed to bring together statisticians and other interested parties who are actively engaged in helping to solve the nation's energy problems. The program included presentations of technical papers centered around exploration and disposal of nuclear fuel, general energy-related topics, and health-related issues, and workshops on model evaluation, risk analysis, analysis of large data sets, and resource estimation.

  13. A Random Algorithm for Low-Rank Decomposition of Large-Scale Matrices With Missing Entries.

    PubMed

    Liu, Yiguang; Lei, Yinjie; Li, Chunguang; Xu, Wenzheng; Pu, Yifei

    2015-11-01

    A random submatrix method (RSM) is proposed to calculate the low-rank decomposition U(m×r)V(n×r)(T) (r < m, n) of the matrix Y∈R(m×n) (assuming m > n generally) with known entry percentage 0 < ρ ≤ 1. RSM is very fast as only O(mr(2)ρ(r)) or O(n(3)ρ(3r)) floating-point operations (flops) are required, compared favorably with O(mnr+r(2)(m+n)) flops required by the state-of-the-art algorithms. Meanwhile, RSM has the advantage of a small memory requirement as only max(n(2),mr+nr) real values need to be saved. With the assumption that known entries are uniformly distributed in Y, submatrices formed by known entries are randomly selected from Y with statistical size k×nρ(k) or mρ(l)×l , where k or l takes r+1 usually. We propose and prove a theorem, under random noises the probability that the subspace associated with a smaller singular value will turn into the space associated to anyone of the r largest singular values is smaller. Based on the theorem, the nρ(k)-k null vectors or the l-r right singular vectors associated with the minor singular values are calculated for each submatrix. The vectors ought to be the null vectors of the submatrix formed by the chosen nρ(k) or l columns of the ground truth of V(T). If enough submatrices are randomly chosen, V and U can be estimated accordingly. The experimental results on random synthetic matrices with sizes such as 13 1072 ×10(24) and on real data sets such as dinosaur indicate that RSM is 4.30 ∼ 197.95 times faster than the state-of-the-art algorithms. It, meanwhile, has considerable high precision achieving or approximating to the best. PMID:26208344

  14. Fuel cells: Technology status and applications; Proceedings of the Symposium, Chicago, IL, November 16-18, 1981

    NASA Astrophysics Data System (ADS)

    Progress, experimentation, results from prototype construction and operations, and future directions for fuel cell construction, research, and applications are discussed. The basic theory, construction, and functioning principles of fuels cells are reviewed, and attention is given to solid polymer, phosphoric acid, molten carbonate, and solid oxide fuel cells. Fuel processing for fuel cells is examined, along with the economics and features of fuel cell uses by utilities, the military, municipalities, and for transportation. Specific mention is made of fuel cells in industrial cogeneration installations and technical and economic problems which must be solved to bring fuel cells to commercial readiness are described.

  15. Improving synthesis and analysis prior blind compressed sensing with low-rank constraints for dynamic MRI reconstruction.

    PubMed

    Majumdar, Angshul

    2015-01-01

    In blind compressed sensing (BCS), both the sparsifying dictionary and the sparse coefficients are estimated simultaneously during signal recovery. A recent study adopted the BCS framework for recovering dynamic MRI sequences from under-sampled K-space measurements; the results were promising. Previous works in dynamic MRI reconstruction showed that, recovery accuracy can be improved by incorporating low-rank penalties into the standard compressed sensing (CS) optimization framework. Our work is motivated by these studies, and we improve upon the basic BCS framework by incorporating low-rank penalties into the optimization problem. The resulting optimization problem has not been solved before; hence we derive a Split Bregman type technique to solve the same. Experiments were carried out on real dynamic contrast enhanced MRI sequences. Results show that, with our proposed improvement, the reconstruction accuracy is better than BCS and other state-of-the-art dynamic MRI recovery algorithms. PMID:25179137

  16. Effect of pretreatment with carbonic acid on 'Hypercoal' (ash-free coal) production from low-rank coals

    SciTech Connect

    Kensuke Masaki; Nao Kashimura; Toshimasa Takanohashi; Shinya Sato; Akimitsu Matsumura; Ikuo Saito

    2005-10-01

    The use of 'HyperCoal' (ash-free coal) as feedstock for gas turbines results in higher net power output with lower CO{sub 2} emissions. HyperCoal can be produced by thermal extraction from low-rank coals with industrial organic solvents in an inert atmosphere, providing raw materials. The pretreatment of low-rank coals with carbonic acid (CO{sub 2} dissolved in water - CO{sub 2}/H{sub 2}O) produced a strong increase in HyperCoal yields at relatively lower CO{sub 2} pressures of 0.1-0.5 MPa; the thermal extraction yields at 360{sup o}C increased by 7%-15% with extraction yields of 52% and 45% obtained for Wyodak sub-bituminous coal and Beulah-Zap lignite, respectively. In the range of 320-360{sup o}C, crude methylnaphthalene oil (CMNO) extraction yields of pretreated Wyodak coal increased significantly (by 4%-11%) over those of raw coal. The enhanced extraction yields of these low-rank coals are attributed to disruption of cation-bridging crosslinks on acid pretreatment, and the release of the hydrogen bonds by CMNO extraction. 18 refs., 4 figs., 4 tabs.

  17. Symposium Summary

    NASA Astrophysics Data System (ADS)

    Milner, Richard G.

    2016-02-01

    The Stern-Gerlach experiment and the origin of electron spin are described in historical context. SPIN 2014 occurs on the fortieth anniversary of the first International High Energy Spin Physics Symposium at Argonne in 1974. A brief history of the international spin conference series is presented.

  18. Symposium: Assessment

    ERIC Educational Resources Information Center

    Anson, Chris M.; Perelman, Les; Poe, Mya; Sommers, Nancy

    2008-01-01

    This article presents four symposium papers on assessment. It includes: (1) "Closed Systems and Standardized Writing Tests" (Chris M. Anson); (2) "Information Illiteracy and Mass Market Writing Assessments" (Les Perelman); (3) "Genre, Testing, and the Constructed Realities of Student Achievement" (Mya Poe); and (4) "The Call of Research: A…

  19. Harnessing data structure for recovery of randomly missing structural vibration responses time history: Sparse representation versus low-rank structure

    NASA Astrophysics Data System (ADS)

    Yang, Yongchao; Nagarajaiah, Satish

    2016-06-01

    Randomly missing data of structural vibration responses time history often occurs in structural dynamics and health monitoring. For example, structural vibration responses are often corrupted by outliers or erroneous measurements due to sensor malfunction; in wireless sensing platforms, data loss during wireless communication is a common issue. Besides, to alleviate the wireless data sampling or communication burden, certain accounts of data are often discarded during sampling or before transmission. In these and other applications, recovery of the randomly missing structural vibration responses from the available, incomplete data, is essential for system identification and structural health monitoring; it is an ill-posed inverse problem, however. This paper explicitly harnesses the data structure itself-of the structural vibration responses-to address this (inverse) problem. What is relevant is an empirical, but often practically true, observation, that is, typically there are only few modes active in the structural vibration responses; hence a sparse representation (in frequency domain) of the single-channel data vector, or, a low-rank structure (by singular value decomposition) of the multi-channel data matrix. Exploiting such prior knowledge of data structure (intra-channel sparse or inter-channel low-rank), the new theories of ℓ1-minimization sparse recovery and nuclear-norm-minimization low-rank matrix completion enable recovery of the randomly missing or corrupted structural vibration response data. The performance of these two alternatives, in terms of recovery accuracy and computational time under different data missing rates, is investigated on a few structural vibration response data sets-the seismic responses of the super high-rise Canton Tower and the structural health monitoring accelerations of a real large-scale cable-stayed bridge. Encouraging results are obtained and the applicability and limitation of the presented methods are discussed.

  20. Energy and environmental research emphasizing low-rank coal: Task 3.9 catalytic tar cracking

    SciTech Connect

    Timpe, R.C.

    1995-09-01

    Tar produced in the gasification of coal is deleterious to the operation of downstream equipment, including fuel cells, gas turbines, hot-gas stream cleanup filters, and pressure-swing absorption systems. Catalytic cracking of tars to smaller hydrocarbons can be an effective means of removing these tars from gas streams and, in the process, generating useful products, such as methane gas, which is crucial to operation of molten carbonate fuel cells. Aerosol tars are not readily removed from gas streams by conventional means and, as a consequence, often end up plugging filters or fouling fuel cells, turbines, or sorbents. Catalytic cracking of these tars to molecular moieties of C{sub 10} or smaller would prevent the problems commonly attributed to the tars. As an example, the moving Bourdon fixed-bed gasifier, by virtue of its efficient countercurrent heat exchange and widespread commercial use, may offer the lowest-cost integrated gasification combined-cycle (IGCC) system if tar generation and wastewater contamination can be minimized. We evaluate the potential of selected catalysts to minimize tar accumulation and maximize char conversion to useful liquid and/or gaseous products. Owing to the potential for production of extremely toxic nickel carbonyl gas, care must be exercised in the use of a NISMM catalyst for cracking tars at high temperatures in reducing atmospheres such as those produced by coal gasification. We observed a fifty percent or more of tar produced during steam gasification of Beulah lignite at temperatures of 400{degrees}-800+{degrees}C when cracked by either dolomite or zeolite maintained at a temperature of 50{degrees}C-100{degrees}C below that of the reactor.

  1. Low-rank coal study: national needs for resource development. Volume 6. Peat

    SciTech Connect

    Not Available

    1980-11-01

    The requirements and potential for development of US peat resources for energy use are reviewed. Factors analyzed include the occurrence and properties of major peat deposits; technologies for extraction, dewatering, preparation, combustion, and conversion of peat to solid, liquid, or gaseous fuels; environmental, regulatory, and market constraints; and research, development, and demonstration (RD and D) needs. Based on a review of existing research efforts, recommendations are made for a comprehensive national RD and D program to enhance the use of peat as an energy source.

  2. Low-rank + sparse (L+S) reconstruction for accelerated dynamic MRI with seperation of background and dynamic components

    NASA Astrophysics Data System (ADS)

    Otazo, Ricardo; Sodickson, Daniel K.; Candès, Emmanuel J.

    2013-09-01

    L+S matrix decomposition finds the low-rank (L) and sparse (S) components of a matrix M by solving the following convex optimization problem: min‖L‖*L+S matrix decomposition finds the low-rank (L) and sparse (S) components of a matrix M by solving the following convex optimization problem: ‖L ‖* + λ‖S‖1, subject to M=L+S, where ‖L‖* is the nuclear-norm or sum of singular values of L and ‖S‖1 is the 11-norm| or sum of absolute values of S. This work presents the application of the L+S decomposition to reconstruct incoherently undersampled dynamic MRI data as a superposition of a slowly or coherently changing background and sparse innovations. Feasibility of the method was tested in several accelerated dynamic MRI experiments including cardiac perfusion, time-resolved peripheral angiography and liver perfusion using Cartesian and radial sampling. The high acceleration and background separation enabled by L+S reconstruction promises to enhance spatial and temporal resolution and to enable background suppression without the need of subtraction or modeling.

  3. The study to change in the molecule structure of low rank coal by using of low temperature pyrolysis deoxygen

    SciTech Connect

    Zhongshu, D.; Yuhui, Z.; Lihong, M.

    1999-07-01

    A series of experiments were performed on the oxygen functional group of low rank coal by use of the low temperature pyrolysis Deoxy method. The order of chemical activity of oxygen functional group in coal was obtained; also the results show the redistribution of the oxygen in the coals and a little decrease of sulfur and nitrogen. In addition to FTIR, {sup 1}H-NMR, {sup 13}C-NMR, accelerated surface area and porosimetry distribution were used to characterize coal pyrolyzed at various low temperatures. The results have shown that the distribution of hydrogen, carbon and porosity of the coal are also changed along with the deoxidization. It is suggested that the chemical and physical structures of the coal molecule are changed, which leads to modification of coal. The tests of coking properties of single coals and compatibility of blend before and after the low temperature pyrolysis treatment were studied. The results indicate that the low temperature pyrolysis deoxygen can improve utilization of low rank coal.

  4. Low-rank and Sparse Matrix Decomposition for Accelerated Dynamic MRI with Separation of Background and Dynamic Components

    PubMed Central

    Otazo, Ricardo; Candès, Emmanuel; Sodickson, Daniel K.

    2014-01-01

    Purpose To apply the low-rank plus sparse (L+S) matrix decomposition model to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest. Theory and Methods The L+S model is natural to represent dynamic MRI data. Incoherence between k−t space (acquisition) and the singular vectors of L and the sparse domain of S is required to reconstruct undersampled data. Incoherence between L and S is required for robust separation of background and dynamic components. Multicoil L+S reconstruction is formulated using a convex optimization approach, where the nuclear-norm is used to enforce low-rank in L and the l1-norm to enforce sparsity in S. Feasibility of the L+S reconstruction was tested in several dynamic MRI experiments with true acceleration including cardiac perfusion, cardiac cine, time-resolved angiography, abdominal and breast perfusion using Cartesian and radial sampling. Results The L+S model increased compressibility of dynamic MRI data and thus enabled high acceleration factors. The inherent background separation improved background suppression performance compared to conventional data subtraction, which is sensitive to motion. Conclusion The high acceleration and background separation enabled by L+S promises to enhance spatial and temporal resolution and to enable background suppression without the need of subtraction or modeling. PMID:24760724

  5. Conversion of Low-Rank Wyoming Coals into Gasoline by Direct Liquefaction

    SciTech Connect

    Polyakov, Oleg

    2013-12-31

    Under the cooperative agreement program of DOE and funding from Wyoming State’s Clean Coal Task Force, Western Research Institute and Thermosolv LLC studied the direct conversion of Wyoming coals and coal-lignin mixed feeds into liquid fuels in conditions highly relevant to practice. During the Phase I, catalytic direct liquefaction of sub-bituminous Wyoming coals was investigated. The process conditions and catalysts were identified that lead to a significant increase of desirable oil fraction in the products. The Phase II work focused on systematic study of solvothermal depolymerization (STD) and direct liquefaction (DCL) of carbonaceous feedstocks. The effect of the reaction conditions (the nature of solvent, solvent/lignin ratio, temperature, pressure, heating rate, and residence time) on STD was investigated. The effect of a number of various additives (including lignin, model lignin compounds, lignin-derivable chemicals, and inorganic radical initiators), solvents, and catalysts on DCL has been studied. Although a significant progress has been achieved in developing solvothermal depolymerization, the side reactions – formation of considerable amounts of char and gaseous products – as well as other drawbacks do not render aqueous media as the most appropriate choice for commercial implementation of STD for processing coals and lignins. The trends and effects discovered in DCL point at the specific features of liquefaction mechanism that are currently underutilized yet could be exploited to intensify the process. A judicious choice of catalysts, solvents, and additives might enable practical and economically efficient direct conversion of Wyoming coals into liquid fuels.

  6. Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix.

    PubMed

    Zhang, Haicang; Gao, Yujuan; Deng, Minghua; Wang, Chao; Zhu, Jianwei; Li, Shuai Cheng; Zheng, Wei-Mou; Bu, Dongbo

    2016-03-25

    Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix. We trained our LRS-based method on the PSICOV dataset, and tested it on both GREMLIN and CASP11 datasets. Our experimental results suggested that LRS significantly improves the contact prediction precision. For example, when equipped with the LRS technique, the prediction precision of MI and mfDCA increased from 0.25 to 0.67 and from 0.58 to 0.70, respectively (Top L/10 predicted contacts, sequence separation: 5 AA, dataset: GREMLIN). In addition, our LRS technique also consistently outperforms the popular denoising technique APC (average product correction), on both local (MI_LRS: 0.67 vs MI_APC: 0.34) and global measures (mfDCA_LRS: 0.70 vs mfDCA_APC: 0.67). Interestingly, we found out that when equipped with our LRS technique, local inference strategies performed in a comparable manner to that of global inference strategies, implying that the application of LRS technique narrowed down the performance gap between local and global inference strategies. Overall, our LRS technique greatly facilitates

  7. Symposium on Batteries and Fuel Cells for Stationary and Electric Vehicle Applications, Honolulu, HI, May 16-21, 1993, Proceedings

    NASA Astrophysics Data System (ADS)

    Landgrebe, Albert R.; Takehara, Zen-Ichiro

    The present conference discusses the development status of vehicular batteries in Japan, the effects of the solvent for electropolymerization of aniline on the charge/discharge characteristics of polyaniline, the charge/discharge mechanism of the amorphous FeOOH, including aniline as a cathode for a rechargeable Li battery, the effect of mesocarbon microbead structure on the electrochemistry of Li secondary batteries' negative electrode, and novel aluminum batteries. Also discussed are a room-temperature molten salt electrolyte for the Na/iron chloride battery, portable cells for redox batteries, the development status of lead-acid batteries for electric vehicles, mechanically refuelable zinc/air vehicular cells, polymer electrolyte fuel cells for transportation applications, proton exchange membrane fuel cells using gas-fed methanol, and a phosphotic acid fuel cell/battery.

  8. Drogue detection for vision-based autonomous aerial refueling via low rank and sparse decomposition with multiple features

    NASA Astrophysics Data System (ADS)

    Gao, Shibo; Cheng, Yongmei; Song, Chunhua

    2013-09-01

    The technology of vision-based probe-and-drogue autonomous aerial refueling is an amazing task in modern aviation for both manned and unmanned aircraft. A key issue is to determine the relative orientation and position of the drogue and the probe accurately for relative navigation system during the approach phase, which requires locating the drogue precisely. Drogue detection is a challenging task due to disorderly motion of drogue caused by both the tanker wake vortex and atmospheric turbulence. In this paper, the problem of drogue detection is considered as a problem of moving object detection. A drogue detection algorithm based on low rank and sparse decomposition with local multiple features is proposed. The global and local information of drogue is introduced into the detection model in a unified way. The experimental results on real autonomous aerial refueling videos show that the proposed drogue detection algorithm is effective.

  9. Dynamic PET reconstruction using temporal patch-based low rank penalty for ROI-based brain kinetic analysis

    NASA Astrophysics Data System (ADS)

    Kim, Kyungsang; Son, Young Don; Bresler, Yoram; Cho, Zang Hee; Ra, Jong Beom; Ye, Jong Chul

    2015-03-01

    Dynamic positron emission tomography (PET) is widely used to measure changes in the bio-distribution of radiopharmaceuticals within particular organs of interest over time. However, to retain sufficient temporal resolution, the number of photon counts in each time frame must be limited. Therefore, conventional reconstruction algorithms such as the ordered subset expectation maximization (OSEM) produce noisy reconstruction images, thus degrading the quality of the extracted time activity curves (TACs). To address this issue, many advanced reconstruction algorithms have been developed using various spatio-temporal regularizations. In this paper, we extend earlier results and develop a novel temporal regularization, which exploits the self-similarity of patches that are collected in dynamic images. The main contribution of this paper is to demonstrate that the correlation of patches can be exploited using a low-rank constraint that is insensitive to global intensity variations. The resulting optimization framework is, however, non-Lipschitz and non-convex due to the Poisson log-likelihood and low-rank penalty terms. Direct application of the conventional Poisson image deconvolution by an augmented Lagrangian (PIDAL) algorithm is, however, problematic due to its large memory requirements, which prevents its parallelization. Thus, we propose a novel optimization framework using the concave-convex procedure (CCCP) by exploiting the Legendre-Fenchel transform, which is computationally efficient and parallelizable. In computer simulation and a real in vivo experiment using a high-resolution research tomograph (HRRT) scanner, we confirm that the proposed algorithm can improve image quality while also extracting more accurate region of interests (ROI) based kinetic parameters. Furthermore, we show that the total reconstruction time for HRRT PET is significantly accelerated using our GPU implementation, which makes the algorithm very practical in clinical environments.

  10. Future fuels for general aviation; Proceedings of the Symposium on Future Fuels for General Aviation Intermittent Combustion, Baltimore, MD, June 29, 1988

    SciTech Connect

    Strauss, K.H.; Gonzalez, C.

    1989-01-01

    The conference presents papers on motor gasoline use in aircraft, alternative fuel use in aircraft, and future fuel requirements. Aircraft field experience with automotive gasoline in the U.S. is considered as well as field experience with type certified civil aircraft operated on motor gasolines and a worldwide survey of motor gasoline characteristics. Attention is also given to the performance of alternative fuels in general aviation aircraft, ethanol and methanol in intermittent combustion engines, and investigations into gasoline/alcohol blends for use in general aviation.

  11. Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm

    NASA Astrophysics Data System (ADS)

    Gomez Gonzalez, C. A.; Absil, O.; Absil, P.-A.; Van Droogenbroeck, M.; Mawet, D.; Surdej, J.

    2016-04-01

    Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. It is a widely used statistical tool developed during the first half of the past century. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Aims: Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. Methods: We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on β Pictoris with VLT/NACO. Results: Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space

  12. Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm

    NASA Astrophysics Data System (ADS)

    Gomez Gonzalez, C. A.; Absil, O.; Absil, P.-A.; Van Droogenbroeck, M.; Mawet, D.; Surdej, J.

    2016-05-01

    Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. It is a widely used statistical tool developed during the first half of the past century. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Aims: Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. Methods: We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on β Pictoris with VLT/NACO. Results: Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space

  13. Fast live cell imaging at nanometer scale using annihilating filter-based low-rank Hankel matrix approach

    NASA Astrophysics Data System (ADS)

    Min, Junhong; Carlini, Lina; Unser, Michael; Manley, Suliana; Ye, Jong Chul

    2015-09-01

    Localization microscopy such as STORM/PALM can achieve a nanometer scale spatial resolution by iteratively localizing fluorescence molecules. It was shown that imaging of densely activated molecules can accelerate temporal resolution which was considered as major limitation of localization microscopy. However, this higher density imaging needs to incorporate advanced localization algorithms to deal with overlapping point spread functions (PSFs). In order to address this technical challenges, previously we developed a localization algorithm called FALCON1, 2 using a quasi-continuous localization model with sparsity prior on image space. It was demonstrated in both 2D/3D live cell imaging. However, it has several disadvantages to be further improved. Here, we proposed a new localization algorithm using annihilating filter-based low rank Hankel structured matrix approach (ALOHA). According to ALOHA principle, sparsity in image domain implies the existence of rank-deficient Hankel structured matrix in Fourier space. Thanks to this fundamental duality, our new algorithm can perform data-adaptive PSF estimation and deconvolution of Fourier spectrum, followed by truly grid-free localization using spectral estimation technique. Furthermore, all these optimizations are conducted on Fourier space only. We validated the performance of the new method with numerical experiments and live cell imaging experiment. The results confirmed that it has the higher localization performances in both experiments in terms of accuracy and detection rate.

  14. Correlated Spatio-Temporal Data Collection in Wireless Sensor Networks Based on Low Rank Matrix Approximation and Optimized Node Sampling

    PubMed Central

    Piao, Xinglin; Hu, Yongli; Sun, Yanfeng; Yin, Baocai; Gao, Junbin

    2014-01-01

    The emerging low rank matrix approximation (LRMA) method provides an energy efficient scheme for data collection in wireless sensor networks (WSNs) by randomly sampling a subset of sensor nodes for data sensing. However, the existing LRMA based methods generally underutilize the spatial or temporal correlation of the sensing data, resulting in uneven energy consumption and thus shortening the network lifetime. In this paper, we propose a correlated spatio-temporal data collection method for WSNs based on LRMA. In the proposed method, both the temporal consistence and the spatial correlation of the sensing data are simultaneously integrated under a new LRMA model. Moreover, the network energy consumption issue is considered in the node sampling procedure. We use Gini index to measure both the spatial distribution of the selected nodes and the evenness of the network energy status, then formulate and resolve an optimization problem to achieve optimized node sampling. The proposed method is evaluated on both the simulated and real wireless networks and compared with state-of-the-art methods. The experimental results show the proposed method efficiently reduces the energy consumption of network and prolongs the network lifetime with high data recovery accuracy and good stability. PMID:25490583

  15. Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study.

    PubMed

    Cai, Jian-Feng; Jia, Xun; Gao, Hao; Jiang, Steve B; Shen, Zuowei; Zhao, Hongkai

    2014-08-01

    Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over one breathing cycle and the fourth dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to obtain true 4DCBCT with the fourth dimension being time, i.e., each constituent CBCT image corresponds to an instantaneous projection. Theoretically it is impossible to reconstruct a CBCT image from a single projection. However, if all the constituent CBCT images of a 4DCBCT scan share a lot of redundant information, it might be possible to make a good reconstruction of these images by exploring their sparsity and coherence/redundancy. Though these CBCT images are not completely time resolved, they can exploit both local and global temporal coherence of the patient anatomy automatically and contain much more temporal variation information of the patient geometry than the conventional 4DCBCT. We propose in this work a computational model and algorithms for the reconstruction of this type of semi-time-resolved CBCT, called cine-CBCT, based on low rank approximation that can utilize the underlying temporal coherence both locally and globally, such as slow variation, periodicity or repetition, in those cine-CBCT images. PMID:24771574

  16. Scoping Studies to Evaluate the Benefits of an Advanced Dry Feed System on the Use of Low-Rank Coal

    SciTech Connect

    Rader, Jeff; Aguilar, Kelly; Aldred, Derek; Chadwick, Ronald; Conchieri,; Dara, Satyadileep; Henson, Victor; Leininger, Tom; Liber, Pawel; Nakazono, Benito; Pan, Edward; Ramirez, Jennifer; Stevenson, John; Venkatraman, Vignesh

    2012-11-30

    This report describes the development of the design of an advanced dry feed system that was carried out under Task 4.0 of Cooperative Agreement DE-FE0007902 with the US DOE, “Scoping Studies to Evaluate the Benefits of an Advanced Dry Feed System on the use of Low- Rank Coal.” The resulting design will be used for the advanced technology IGCC case with 90% carbon capture for sequestration to be developed under Task 5.0 of the same agreement. The scope of work covered coal preparation and feeding up through the gasifier injector. Subcomponents have been broken down into feed preparation (including grinding and drying), low pressure conveyance, pressurization, high pressure conveyance, and injection. Pressurization of the coal feed is done using Posimetric1 Feeders sized for the application. In addition, a secondary feed system is described for preparing and feeding slag additive and recycle fines to the gasifier injector. This report includes information on the basis for the design, requirements for down selection of the key technologies used, the down selection methodology and the final, down selected design for the Posimetric Feed System, or PFS.

  17. Low-rank approximation based non-negative multi-way array decomposition on event-related potentials.

    PubMed

    Cong, Fengyu; Zhou, Guoxu; Astikainen, Piia; Zhao, Qibin; Wu, Qiang; Nandi, Asoke K; Hietanen, Jari K; Ristaniemi, Tapani; Cichocki, Andrzej

    2014-12-01

    Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD and HALS NCPD were very similar, but LRAHALS NCPD was 70 times faster than HALS NCPD. Moreover, the desired multi-domain feature of the ERP by NCPD showed a significant group difference (control versus depressed participants) and a difference in emotion processing (fearful versus happy faces). This was more satisfactory than that by CPD, which revealed only a group difference. PMID:25164246

  18. A reduced basis approach for calculation of the Bethe–Salpeter excitation energies by using low-rank tensor factorisations

    NASA Astrophysics Data System (ADS)

    Benner, Peter; Khoromskaia, Venera; Khoromskij, Boris N.

    2016-04-01

    The Bethe-Salpeter equation (BSE) is a reliable model for estimating the absorption spectra in molecules and solids on the basis of accurate calculation of the excited states from first principles. This challenging task includes calculation of the BSE operator in terms of two-electron integrals tensor represented in molecular orbital basis, and introduces a complicated algebraic task of solving the arising large matrix eigenvalue problem. The direct diagonalization of the BSE matrix is practically intractable due to $O(N^6)$ complexity scaling in the size of the atomic orbitals basis set, $N$. In this paper, we present a new approach to the computation of Bethe-Salpeter excitation energies which can lead to relaxation of the numerical costs up to $O(N^3)$. The idea is twofold: first, the diagonal plus low-rank tensor approximations to the fully populated blocks in the BSE matrix is constructed, enabling easier partial eigenvalue solver for a large auxiliary system relying only on matrix-vector multiplications with rank-structured matrices. And second, a small subset of eigenfunctions from the auxiliary eigenvalue problem is selected to build the Galerkin projection of the exact BSE system onto the reduced basis set. We present numerical tests on BSE calculations for a number of molecules confirming the $\\varepsilon$-rank bounds for the blocks of BSE matrix. The numerics indicates that the reduced BSE eigenvalue problem with small matrices enables calculation of the lowest part of the excitation spectrum with sufficient accuracy.

  19. A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety.

    PubMed

    Zhang, Zutao; Li, Yanjun; Wang, Fubing; Meng, Guanjun; Salman, Waleed; Saleem, Layth; Zhang, Xiaoliang; Wang, Chunbai; Hu, Guangdi; Liu, Yugang

    2016-01-01

    Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. PMID:27294931

  20. A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety

    PubMed Central

    Zhang, Zutao; Li, Yanjun; Wang, Fubing; Meng, Guanjun; Salman, Waleed; Saleem, Layth; Zhang, Xiaoliang; Wang, Chunbai; Hu, Guangdi; Liu, Yugang

    2016-01-01

    Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. PMID:27294931

  1. Investigation of the role of aromatic carboxylic acids in cross-linking processes in low-rank coals

    SciTech Connect

    Eskay, T.P.; Britt, P.F.; Buchanan, A.C. III

    1997-03-01

    In the pyrolysis and liquefaction of low-rank coals, low-temperature cross-linking reactions have been correlated with the loss of carboxyl groups and the evolution of CO{sub 2} and H{sub 2}O. It is not clearly understood how decarboxylation leads to cross-linking beyond the suggestion that decarboxylation could be a radical process that involves radical recombination or radical addition reactions. We have recently conducted a study of the pyrolysis of 1,2-(3,3{prime}-dicarboxyphenyl)ethane (1) and 1,2-(4,4{prime}-dicarboxyphenyl)ethane (2) and found that decarboxylation occurs readily between 350-425 {degrees}C with no evidence of coupling products or products representative of cross-links. We proposed that decarboxylation occurred primarily by an acid-promoted cationic pathway, and the source of acid was a second carboxylic acid. The decarboxylation of 1 and 2 was investigated in diphenyl ether and naphthalene as inert diluents. In each solvent, the rate of decarboxylation dropped by roughly a factor of 2 upon dilution from the neat liquid to ca. 0.4 mole fraction of acid, but further dilution had no effect on the rate. This could be a consequence of hydrogen bonding or an intramolecular protonation. Molecular mechanics calculations indicated that 1 and 2 can adopt an appropriate conformation for internal proton transfer from a carboxy group on one ring to the second aryl ring without a significant energy penalty. In addition, the dicarboxylic acid could internally hydrogen bond, which may further complicate the reaction mechanism. Therefore, we have conducted a study of the pyrolysis of a monocarboxybibenzyl, 1-(3-carboxyphenyl)-2-(4-biphenyl)ethane (3), to determine if decarboxylation occurs by an ionic pathway in the absence of intramolecular pathways.

  2. Application of a boiler performance model to evaluate low-rank coal fired subcritical and supercritical boilers

    SciTech Connect

    Ahn, Y.K.; Buchanan, T.L.; Zaharchuk, R.

    1995-12-31

    A number of thermal drying processes that could be used to dry and upgrade Low-Rank Coals (LRCs) are under development. G/C evaluated these processes and selected the SynCoal process as the optimum process to dry the LRC. Initially, the evaluation was made on the basis of the cost of dried LRC, delivered to Korea, and later the evaluation was made on a cost-of-electricity (COE) basis. Two cases were evaluated: firing the dried LRC in an existing subcritical PC plant and in a new supercritical boiler. For the existing PC plant, Korea Electric Power Corporation`s (KEPCO`s) 270 MWe Honam plant was selected. A Boiler Performance Model (BPM) was used to evaluate performances of both subcritical and supercritical units for firing various coals. The results showed that upgraded Usibelli coal was marginally competitive due to its high mine-mouth cost, but Rosebud coal was very competitive due to its low mine-mouth cost. In these cases the coals were upgraded by using the SynCoal process. This report investigates the impact of tax incentives resulting from the Energy Policy Act of 1992 on the competitiveness of the upgraded Alaska Usibelli and Montana Rosebud coals for application to PC plants. The SynCoal process has been qualified by the Internal Revenue Service for tax benefits derived from the Energy Policy Act. The economic analyses include costs and sensitivity analyses for alternative ways of selling fines produced during the SynCoal process: briquetting fines and adding them to the finished product, or cooling fines and selling them to users at the same price as SynCoal product in the domestic market. These analyses included the effects of tax incentive when applicable.

  3. Polynomial meta-models with canonical low-rank approximations: Numerical insights and comparison to sparse polynomial chaos expansions

    NASA Astrophysics Data System (ADS)

    Konakli, Katerina; Sudret, Bruno

    2016-09-01

    The growing need for uncertainty analysis of complex computational models has led to an expanding use of meta-models across engineering and sciences. The efficiency of meta-modeling techniques relies on their ability to provide statistically-equivalent analytical representations based on relatively few evaluations of the original model. Polynomial chaos expansions (PCE) have proven a powerful tool for developing meta-models in a wide range of applications; the key idea thereof is to expand the model response onto a basis made of multivariate polynomials obtained as tensor products of appropriate univariate polynomials. The classical PCE approach nevertheless faces the "curse of dimensionality", namely the exponential increase of the basis size with increasing input dimension. To address this limitation, the sparse PCE technique has been proposed, in which the expansion is carried out on only a few relevant basis terms that are automatically selected by a suitable algorithm. An alternative for developing meta-models with polynomial functions in high-dimensional problems is offered by the newly emerged low-rank approximations (LRA) approach. By exploiting the tensor-product structure of the multivariate basis, LRA can provide polynomial representations in highly compressed formats. Through extensive numerical investigations, we herein first shed light on issues relating to the construction of canonical LRA with a particular greedy algorithm involving a sequential updating of the polynomial coefficients along separate dimensions. Specifically, we examine the selection of optimal rank, stopping criteria in the updating of the polynomial coefficients and error estimation. In the sequel, we confront canonical LRA to sparse PCE in structural-mechanics and heat-conduction applications based on finite-element solutions. Canonical LRA exhibit smaller errors than sparse PCE in cases when the number of available model evaluations is small with respect to the input dimension, a

  4. Joint Diagnosis and Conversion Time Prediction of Progressive Mild Cognitive Impairment (pMCI) Using Low-Rank Subspace Clustering and Matrix Completion

    PubMed Central

    Thung, Kim-Han; Yap, Pew-Thian; Adeli-M, Ehsan; Shen, Dinggang

    2015-01-01

    Identifying progressive mild cognitive impairment (pMCI) patients and predicting when they will convert to Alzheimer’s disease (AD) are important for early medical intervention. Multi-modality and longitudinal data provide a great amount of information for improving diagnosis and prognosis. But these data are often incomplete and noisy. To improve the utility of these data for prediction purposes, we propose an approach to denoise the data, impute missing values, and cluster the data into low-dimensional subspaces for pMCI prediction. We assume that the data reside in a space formed by a union of several low-dimensional subspaces and that similar MCI conditions reside in similar subspaces. Therefore, we first use incomplete low-rank representation (ILRR) and spectral clustering to cluster the data according to their representative low-rank subspaces. At the same time, we denoise the data and impute missing values. Then we utilize a low-rank matrix completion (LRMC) framework to identify pMCI patients and their time of conversion. Evaluations using the ADNI dataset indicate that our method outperforms conventional LRMC method. PMID:27054201

  5. Energy and environmental research emphasizing low-rank coal. Semi-annual report, January--June 1994

    SciTech Connect

    1994-09-01

    Summaries of progress on the following tasks are presented: Mixed waste treatment; Hot water extraction of nonpolar organic pollutant from soils; Aqueous phase thermal oxidation wastewater treatment; Review of results from comprehensive characterization of air toxic emissions from coal-fired power plants; Air toxic fine particulate control; Effectiveness of sorbents for trace elements; Catalyst for utilization of methane in selective catalytic reduction of NOx; Fuel utilization properties; Hot gas cleaning; PFBC; catalytic tar cracking; sulfur forms in coal; resid and bitumen desulfurization; biodesulfurization; diesel fuel desulfurization; stability issues; Sorbent carbon development; Evaluation of carbon products; Stable and supercritical chars; Briquette binders; Carbon molecular sieves; Coal char fuel evaporation canister sorbent; Development of a coal by-product classification protocol for utilization; Use of coal ash in recycled plastics and composite materials; Corrosion of advanced structural materials; Joining of advanced structural materials; Resource data evaluation; and the Usti and Labem (Czech Republic) coal-upgrading program.

  6. Comparative study of thermal properties of bio-coal from aromatic spent with low rank sub-bituminous coals.

    PubMed

    Yadav, Vineet; Baruah, B P; Khare, Puja

    2013-06-01

    In present investigation, biocoal samples were prepared from aromatic plant waste of two perennial grasses, i.e. Cymbopogon flexuosus (lemongrass) and Vetiveria zizanioides (khus) after oil extraction, root of Rosa damascene (rose), bark of Eucalyptus citriodora. These biocoals were characterized by proximate, ultimate, metal, thermogravimetric analysis (TGA), Fourier Transform Infra Red (FTIR) spectroscopy and ash analyses. Activation energies, initial temperature of devolatilization, maximum rate of weight loss (Rmax), fouling, slagging and alkali index were determined on the basis of TGA and ash analysis. These biocoals have good calorific values. There is possibility of slagging and fouling in combustion system but it is not severe. Owing to their similar fuel properties as high sulphur sub-bituminous coal, they can be good candidates for co-firing. Blending of these biocoals with high sulphur coals will serve dual purpose as (i) alternate fuel, and (ii) reduction in SO2 emission. PMID:23603187

  7. Space Symposium/76

    NASA Technical Reports Server (NTRS)

    1976-01-01

    A symposium dealing with career opportunities in the aerospace program for minorities was conducted and evaluated. The symposium was attended by students from eleven predominantly minority colleges and universities in and around Washington, D. C. and the eastern region, and from high schools in five jurisdictions of the Washington metropolitan area. Speakers included representatives of Howard University, NASA, and private industry. On display during the symposium was a NASA exhibit of moon rocks, space shuttles, a lunar module, command module, pacemaker, LANDSAT, and other items of interest.

  8. Ninteenth Aerospace Mechanisms Symposium

    NASA Technical Reports Server (NTRS)

    1985-01-01

    The proceedings of the 19th Aerospace Mechanisms Symposium are reported. Technological areas covered include space lubrication, bearings, aerodynamic devices, spacecraft/Shuttle latches, deployment, positioning, and pointing. Devices for spacecraft docking and manipulator and teleoperator mechanisms are also described.

  9. A primer on the occurrence of coalbed methane in low-rank coals, with special reference to its potential occurrence in Pakistan

    USGS Publications Warehouse

    SanFilipo, John R.

    2000-01-01

    Introduction: This report compiles and updates a series of correspondence that took place between 1998 and early 2000 among the author and representatives of various consulting groups operating in the coal sector of Pakistan. The purpose of the original correspondence was to introduce basic concepts of coalbed methane (CBM) in low-rank coals to planners and other parties interested in the development of Pakistan's coal, particularly the large deposits of the Thar desert area of Sindh Province that were recently discovered (SanFilipo and Khan, 1994) by the Geological Survey of Pakistan (GSP) and the U.S. Geological Survey (USGS). The author tested two shallow boreholes in Sindh Province for CBM in 1992, including one in Thar, with very marginal results. Additional targets with better CBM prospects were recommended shortly thereafter (SanFilipo and others, 1994), but these were not followed up during subsequent drilling, nor were any other sites tested. Recent events, notably the rapid pace of CBM development in low-rank coals of the Powder River Basin of the U.S., and a show of CBM in commercial quantities in the Cambay Basin of India - both of which are similar in age and rank to most of Pakistan's coal - have indicated a need for reevaluating the initial CBM investigations made in Pakistan in 1992 and for a reassessment of the CBM prospects for the country at large.

  10. CeO2-TiO2 catalysts for catalytic oxidation of elemental mercury in low-rank coal combustion flue gas.

    PubMed

    Li, Hailong; Wu, Chang-Yu; Li, Ying; Zhang, Junying

    2011-09-01

    CeO(2)-TiO(2) (CeTi) catalysts synthesized by an ultrasound-assisted impregnation method were employed to oxidize elemental mercury (Hg(0)) in simulated low-rank (sub-bituminous and lignite) coal combustion flue gas. The CeTi catalysts with a CeO(2)/TiO(2) weight ratio of 1-2 exhibited high Hg(0) oxidation activity from 150 to 250 °C. The high concentrations of surface cerium and oxygen were responsible for their superior performance. Hg(0) oxidation over CeTi catalysts was proposed to follow the Langmuir-Hinshelwood mechanism whereby reactive species from adsorbed flue gas components react with adjacently adsorbed Hg(0). In the presence of O(2), a promotional effect of HCl, NO, and SO(2) on Hg(0) oxidation was observed. Without O(2), HCl and NO still promoted Hg(0) oxidation due to the surface oxygen, while SO(2) inhibited Hg(0) adsorption and subsequent oxidation. Water vapor also inhibited Hg(0) oxidation. HCl was the most effective flue gas component responsible for Hg(0) oxidation. However, the combination of SO(2) and NO without HCl also resulted in high Hg(0) oxidation efficiency. This superior oxidation capability is advantageous to Hg(0) oxidation in low-rank coal combustion flue gas with low HCl concentration. PMID:21770402

  11. 1999 Flight Mechanics Symposium

    NASA Technical Reports Server (NTRS)

    Lynch, John P. (Editor)

    1999-01-01

    This conference publication includes papers and abstracts presented at the Flight Mechanics Symposium held on May 18-20, 1999. Sponsored by the Guidance, Navigation and Control Center of Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to orbit-attitude prediction, determination, and control; attitude sensor calibration; attitude determination error analysis; attitude dynamics; and orbit decay and maneuver strategy. Government, industry, and the academic community participated in the preparation and presentation of these papers.

  12. ACS Symposium Support

    SciTech Connect

    Kenneth D. Jordan

    2010-02-20

    The funds from this DOE grant were used to help cover the travel costs of five students and postdoctoral fellows who attended a symposium on 'Hydration: From Clusters to Aqueous Solutions' held at the Fall 2007 American Chemical Society Meeting in Boston, MA, August 19-23. The Symposium was sponsored by the Physical Chemistry Division, ACS. The technical program for the meeting is available at http://phys-acs.org/fall2007.html.

  13. PROCEEDINGS OF THE 2003 NATIONAL OILHEAT RESEARCH ALLIANCE TECHNOLOGY SYMPOSIUM, HELD AT THE 2003 NEW ENGLAND FUEL INSTITUTE CONVENTION AND 30TH NORTH AMERICAN HEATING AND ENERGY EXPOSITION, HYNES CONVENTION CENTER, PRUDENTIAL CENTER, BOSTON, MASSACHUSETTS, JUNE 9 - 10, 2003.

    SciTech Connect

    MCDONALD,R.J.

    2003-06-09

    This meeting is the sixteenth oilheat industry technology meeting held since 1984 and the third since the National Oilheat Research Alliance (NORA) was formed. This year's symposium is a very important part of the effort in technology transfer, which is supported by the Oilheat Research Fuel Flexibility Program under the United States Department of Energy, Distributed Energy and Electricity Reliability Program (DEER). The foremost reason for the conference is to provide a platform for the exchange of information and perspectives among international researchers, engineers, manufacturers, service technicians, and marketers of oil-fired space-conditioning equipment. The conference provides a conduit by which information and ideas can be exchanged to examine present technologies, as well as helping to develop the future course for oil heating advancement. These conferences also serve as a stage for unifying government representatives, researchers, fuel oil marketers, and other members of the oil-heat industry in addressing technology advancements in this important energy use sector. The specific objectives of the conference are to: (1) Identify and evaluate the current state-of-the-art and recommend new initiatives for higher efficiency, a cleaner environment, and to satisfy consumer needs cost effectively, reliably, and safely; (2) Foster cooperative interactions among federal and industrial representatives for the common goal of sustained economic growth and energy security via energy conservation.

  14. Energy and environmental research emphasizing low-rank coal -- Task 3.8, Pressurized fluidized-bed combustion

    SciTech Connect

    Mann, M.D.; Henderson, A.K.; Swanson, M.L.

    1995-03-01

    The goal of the PFBC activity is to generate fundamental process information that will further the development of an economical and environmentally acceptable second-generation PFBC. The immediate objectives focus on generic issues, including the performance of sulfur sorbents, fate of alkali, and the Resource Conservation and Recovery Act (RCRA) heavy metals in PFBC. A great deal of PFBC performance relates to the chemistry of the bed and the contact between gas and solids that occurs during combustion. These factors can be studied in a suitably designed bench-scale reactor. The present studies are focusing on the emission control strategies applied in the bed, rather than in hot-gas cleaning. Emission components include alkali and heavy metals in addition to SO{sub 2}, NO{sub x}, N{sub 2}O, and CO. The report presents: a description of the pressurized fluidized-bed reactor (PFBR); a description of the alkali sampling probe; shakedown testing of the bench-scale PFBR; results from alkali sampling; results from sulfur sorbent performance tests; and results from refuse-derived fuel and lignite combustion tests.

  15. AO13. High energy, low methane syngas from low-rank coals for coal-to-liquids production

    SciTech Connect

    Lucero, Andrew; Goyal, Amit; McCabe, Kevin; Gangwal, Santosh

    2015-06-30

    An experimental program was undertaken to develop and demonstrate novel steam reforming catalysts for converting tars, C2+ hydrocarbons, and methane under high temperature and sulfur environments at lab scale. Several catalysts were developed and synthesized along with some catalysts based on recipes found in the literature. Of these, two had good resistance at 90 ppm H2S with one almost not affected at all. Higher concentrations of H2S did affect methane conversion across the catalyst, but performance was fairly stable for up to 200 hours. Based on the results of the experimental program, a techno-economic analysis was developed for IGCC and CTL applications and compared to DOE reference cases to examine the effects of the new technology. In the IGCC cases, the reformer/POX system produces nearly the same amount of electricity for nearly the same cost, however, the reformers/POX case sequesters a higher percentage of the carbon when compared to IGCC alone. For the CTL case the economics of the new process were nearly identical to the CTL case, but due to improved yields, the greenhouse gas emissions for a given production of fuels was approximately 50% less than the baseline case.

  16. 32nd Aerospace Mechanisms Symposium

    NASA Technical Reports Server (NTRS)

    Walker, S. W. (Compiler); Boesiger, Edward A. (Compiler)

    1998-01-01

    The proceedings of the 32nd Aerospace Mechanism Symposium are reported. NASA John F. Kennedy Space Center (KSC) hosted the symposium that was held at the Hilton Oceanfront Hotel in Cocoa Beach, Florida on May 13-15, 1998. The symposium was cosponsored by Lockheed Martin Missiles and Space and the Aerospace Mechanisms Symposium Committee. During these days, 28 papers were presented. Topics included robotics, deployment mechanisms, bearing, actuators, scanners, boom and antenna release, and test equipment.

  17. The VLT Opening Symposium

    NASA Astrophysics Data System (ADS)

    Bergeron, J.

    1999-06-01

    The beginning of the VLT era was marked by two major events: the VLT Official Inauguration Ceremony at Paranal on 5 March 1999, preceded by the VLT Opening Symposium on 1-4 March. ESO is indebted to Professor J.A. Music Tomicic, Rector of the Universidad Católica del Norte, for hosting this symposium. Another major event occurred on the night of 4 March: First light was achieved ahead of schedule at Kueyen, the second 8.2-m VLT unit telescope.

  18. 2001 Flight Mechanics Symposium

    NASA Technical Reports Server (NTRS)

    Lynch, John P. (Editor)

    2001-01-01

    This conference publication includes papers and abstracts presented at the Flight Mechanics Symposium held on June 19-21, 2001. Sponsored by the Guidance, Navigation and Control Center of Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to attitude/orbit determination, prediction and control; attitude simulation; attitude sensor calibration; theoretical foundation of attitude computation; dynamics model improvements; autonomous navigation; constellation design and formation flying; estimation theory and computational techniques; Earth environment mission analysis and design; and, spacecraft re-entry mission design and operations.

  19. Technical Entrepreneurship: A Symposium.

    ERIC Educational Resources Information Center

    Cooper, Arnold C., Ed.; Komives, John L., Ed.

    Contained in this document are papers presented at the Symposium on Technical Entrepreneurship at Purdue University by researchers who were then or had previously been engaged in research in the area. Because formal research in this area was in its infancy, there was a particular need to afford investigators in the field opportunities to compare…

  20. Standards and Certification. Symposium.

    ERIC Educational Resources Information Center

    2002

    This document contains three papers from a symposium on standards and certification in human resource development (HRD). "Implementing Management Standards in the UK" (Jonathan Winterton, Ruth Winterton) reports on a study that explored the implementation of management standards in 16 organizations and identified 36 key themes and strategic issues…

  1. Globalism and HRD. Symposium.

    ERIC Educational Resources Information Center

    2002

    This document contains three papers from a symposium on globalization and human resource development (HRD). "Challenges and Strategies of Developing Human Resources in the Surge of Globalization: A Case of the People's Republic of China" (De Zhang, Baiyin Yang, Yichi Zhang) analyzes the challenges and strategies of HRD in China and discusses the…

  2. Online Learning. Symposium.

    ERIC Educational Resources Information Center

    2002

    This document contains three papers from a symposium on online learning that was conducted as part of a conference on human resource development (HRD). "An Instructional Strategy Framework for Online Learning Environments" (Scott D. Johnson, Steven R. Aragon) discusses the pitfalls of modeling online courses after traditional instruction instead…

  3. Fifth Cooley's anemia symposium

    SciTech Connect

    Bank, A.; Anderson, W.F.; Zaino, E.C.

    1985-01-01

    This book discusses the topics presented at the symposium on the subject of 'Thalassemia'. Sickle cell anemia is also briefly discussed. The aspects discussed are chromosomal defects of anemias particularly globin synthesis, and the role of messenger RNA and other chromosomes.

  4. ASSA Symposium 2012 Abstracts

    NASA Astrophysics Data System (ADS)

    2012-10-01

    of papers presented at the ASSA Symposium held in Cape Town 12-14 October 2012. Videos are available on You tube. See http://www.youtube.com/playlist?list=PL8odLrzpzMkHS-cSEfPFIr3YLPAq4d5MU for a playlist.

  5. Tools in HRD. Symposium.

    ERIC Educational Resources Information Center

    2002

    This document contains three papers from a symposium on tools in human resource development (HRD). "Game Theory Methodology in HRD" (Thomas J. Chermack, Richard A. Swanson) explores the utility of game theory in helping the HRD profession address the complexity of integrating multiple theories for disciplinary understanding and fulfilling its…

  6. Recruitment and Training. Symposium.

    ERIC Educational Resources Information Center

    2002

    This document contains three papers from a symposium on recruitment and training. "College Choice: The State of Marketing and Effective Student Recruitment Strategies" (Fredrick Muyia Nafukho, Michael F. Burnett) reports on a study of the recruitment strategies used by Louisiana State University's admissions office and College of Agriculture that…

  7. European Cosmic Ray Symposium

    ScienceCinema

    None

    2011-04-25

    13me Symposium qui se déroule du 27 au 31 juillet pour la première fois au Cern. Brian Pattison ouvre la cérémonie et donne la parole à Dr.Ugland (qui représente le DG C.Rubbia excusé) et d'autres intervenants

  8. Competencies in HRD. Symposium.

    ERIC Educational Resources Information Center

    2002

    This symposium is comprised of three papers on competencies in human resource development (HRD). "The Development of a Competency Model and Assessment Instrument for Public Sector Leadership and Management Development" (Sharon S. Naquin, Elwood F. Holton III) reports on a streamlined methodology and process used to develop a competency model for…

  9. Quality of Life Symposium.

    ERIC Educational Resources Information Center

    New Mexico State Univ., Las Cruces. New Mexico Environmental Inst.

    Comments, speeches, and questions delivered at the Quality of Life Symposium are compiled in these proceedings. As an exploratory session, the conference objectives were to (1) become better informed about New Mexico--its resource base, the economy, social and cultural base, and the environment; and (2) to evaluate and discuss the role of New…

  10. Values: A Symposium Report.

    ERIC Educational Resources Information Center

    Ryan, T. A., Ed.

    This publication brings together a set of four papers prepared for a symposium on values at the 1972 annual meeting of the American Educational Research Association. The first paper, by Fred N. Kerlinger, establishes a rationale for values research. The discussion focuses on the definition of values, relationship between values and attitudes,…